Gene-gene interaction network analysis indicates CNTN2 is a candidate gene for idiopathic generalized epilepsy.

Twin and family studies have established the genetic contribution to idiopathic generalized epilepsy (IGE). The genetic architecture of IGE is generally complex and heterogeneous, and the majority of the genetic burden in IGE remains unsolved. We hypothesize that gene-gene interactions contribute to the complex inheritance of IGE. CNTN2 (OMIM* 615,400) variants have been identified in cases with familial adult myoclonic epilepsy and other epilepsies. To explore the gene-gene interaction network in IGE, we took the CNTN2 gene as an example and investigated its co-occurrent genetic variants in IGE cases. We performed whole-exome sequencing in 114 unrelated IGE cases and 296 healthy controls. Variants were qualified with sequencing quality, minor allele frequency, in silico prediction, genetic phenotype, and recurrent case numbers. The STRING_TOP25 gene interaction network analysis was introduced with the bait gene CNTN2 (denoted as A). The gene-gene interaction pair mode was presumed to be A + c, A + d, A + e, with a leading gene A, or A + B + f, A + B + g, A + B + h, with a double-gene A + B, or other combinations. We compared the number of gene interaction pairs between the case and control groups. We identified three pairs in the case group, CNTN2 + PTPN18, CNTN2 + CNTN1 + ANK2 + ANK3 + SNTG2, and CNTN2 + PTPRZ1, while we did not discover any pairs in the control group. The number of gene interaction pairs in the case group was much more than in the control group (p = 0.021). Taking together the genetic bioinformatics, reported epilepsy cases, and statistical evidence in the study, we supposed CNTN2 as a candidate pathogenic gene for IGE. The gene interaction network analysis might help screen candidate genes for IGE or other complex genetic disorders.

Sub-region analysis of DMD gene in cases with idiopathic generalized epilepsy.

Gene sub-region encoded protein domain is the basic unit for protein structure and function. The DMD gene is the largest coding gene in humans, with its phenotype relevant to idiopathic generalized epilepsy. We hypothesized variants clustered in sub-regions of idiopathic generalized epilepsy genes and investigated the relationship between the DMD gene and idiopathic generalized epilepsy. Whole exome sequencing was performed in 106 idiopathic generalized epilepsy individuals. DMD variants were filtered with variant type, allele frequency, in silico prediction, hemizygous or homozygous status in the population, inheritance mode, and domain location. Variants located at the sub-regions were selected by the subRVIS software. The pathogenicity of variants was evaluated by the American College of Medical Genetics and Genomics criteria. Articles on functional studies related to epilepsy for variants clustered protein domains were reviewed. In sub-regions of the DMD gene, two variants were identified in two unrelated cases with juvenile absence epilepsy or juvenile myoclonic epilepsy. The pathogenicity of both variants was uncertain significance. Allele frequency of both variants in probands with idiopathic generalized epilepsy reached statistical significance compared with the population (Fisher's test, p = 2.02 × 10-6, adjusted α = 4.52 × 10-6). The variants clustered in the spectrin domain of dystrophin, which binds to glycoprotein complexes and indirectly affects ion channels contributing to epileptogenesis. Gene sub-region analysis suggests a weak association between the DMD gene and idiopathic generalized epilepsy. Functional analysis of gene sub-region helps infer the pathogenesis of idiopathic generalized epilepsy.

A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing.

We analyze visual processing capabilities of a large-scale model for area V1 that arguably provides the most comprehensive accumulation of anatomical and neurophysiological data to date. We find that this brain-like neural network model can reproduce a number of characteristic visual processing capabilities of the brain, in particular the capability to solve diverse visual processing tasks, also on temporally dispersed visual information, with remarkable robustness to noise. This V1 model, whose architecture and neurons markedly differ from those of deep neural networks used in current artificial intelligence (AI), such as convolutional neural networks (CNNs), also reproduces a number of characteristic neural coding properties of the brain, which provides explanations for its superior noise robustness. Because visual processing is substantially more energy efficient in the brain compared with CNNs in AI, such brain-like neural networks are likely to have an impact on future technology: as blueprints for visual processing in more energy-efficient neuromorphic hardware.

A spatiotemporal mechanism of visual attention: Superdiffusive motion and theta oscillations of neural population activity patterns.

Recent evidence has demonstrated that during visual spatial attention sampling, neural activity and behavioral performance exhibit large fluctuations. To understand the origin of these fluctuations and their functional role, here, we introduce a mechanism based on the dynamical activity pattern (attention spotlight) emerging from neural circuit models in the transition regime between different dynamical states. This attention activity pattern with rich spatiotemporal dynamics flexibly samples from different stimulus locations, explaining many key aspects of temporal fluctuations such as variable theta oscillations of visual spatial attention. Moreover, the mechanism expands our understanding of how visual attention exploits spatially complex fluctuations characterized by superdiffusive motion in space and makes experimentally testable predictions. We further illustrate that attention sampling based on such spatiotemporal fluctuations provides profound functional advantages such as adaptive switching between exploitation and exploration activities and is particularly efficient at sampling natural scenes with multiple salient objects.

Anomalous diffusion dynamics of learning in deep neural networks.

Learning in deep neural networks (DNNs) is implemented through minimizing a highly non-convex loss function, typically by a stochastic gradient descent (SGD) method. This learning process can effectively find generalizable solutions at flat minima. In this study, we present a novel account of how such effective deep learning emerges through the interactions of the SGD and the geometrical structure of the loss landscape. We find that the SGD exhibits rich, complex dynamics when navigating through the loss landscape; initially, the SGD exhibits superdiffusion, which attenuates gradually and changes to subdiffusion at long times when approaching a solution. Such learning dynamics happen ubiquitously in different DNN types such as ResNet, VGG-like networks and Vision Transformers; similar results emerge for various batch size and learning rate settings. The superdiffusion process during the initial learning phase indicates that the motion of SGD along the loss landscape possesses intermittent, big jumps; this non-equilibrium property enables the SGD to effectively explore the loss landscape. By adapting methods developed for studying energy landscapes in complex physical systems, we find that such superdiffusive learning processes are due to the interactions of the SGD and the fractal-like regions of the loss landscape. We further develop a phenomenological model to demonstrate the mechanistic role of the fractal-like loss landscape in enabling the SGD to effectively find flat minima. Our results reveal the effectiveness of SGD in deep learning from a novel perspective and have implications for designing efficient deep neural networks.

Computing by modulating spontaneous cortical activity patterns as a mechanism of active visual processing.

Cortical populations produce complex spatiotemporal activity spontaneously without sensory inputs. However, the fundamental computational roles of such spontaneous activity remain unclear. Here, we propose a new neural computation mechanism for understanding how spontaneous activity is actively involved in cortical processing: Computing by Modulating Spontaneous Activity (CMSA). Using biophysically plausible circuit models, we demonstrate that spontaneous activity patterns with dynamical properties, as found in empirical observations, are modulated or redistributed by external stimuli to give rise to neural responses. We find that this CMSA mechanism of generating neural responses provides profound computational advantages, such as actively speeding up cortical processing. We further reveal that the CMSA mechanism provides a unifying explanation for many experimental findings at both the single-neuron and circuit levels, and that CMSA in response to natural stimuli such as face images is the underlying neurophysiological mechanism of perceptual "bubbles" as found in psychophysical studies.

Insight the Luminescence Properties of AlON: Eu, Mg Phosphor under VUV Excitation.

Owing to high quantum efficiency, adjustable composition and antioxidation properties of oxynitride phosphors, extensive investigations have focused on their photoluminescence properties under low-energy light excitation (UV or blue light). However, the vacuum ultraviolet (VUV) luminescence properties of oxynitride phosphors are rarely researched. Present work studies the structure and VUV luminescence properties of an oxynitride phosphor: AlON: Eu, Mg, which is synthesized by solid-state reaction. Under 147 nm excitation, it was found that AlON: Eu, Mg phosphor shows a blue emission band centered at about 470 nm. The first principle calculation is used to analyze the origin of the VUV absorption. Compared with BaMgAl10O17: Eu2+ phosphor, AlON: Eu, Mg phosphor shows better thermal stability.

Enhancement in photoluminescence performance of carbon-decorated T-ZnO.

The facile preparation of ZnO possessing high visible luminescence intensity remains challenging due to an unclear luminescence mechanism. Here, two basic approaches are proposed to enhance the luminescent intensity based on the theoretical analysis over surface defects. Based on the deduction, we introduce a methodology for obtaining hybrid tetrapod-like zinc oxide (T-ZnO), decorated by carbon nanomarterials on T-ZnO surfaces through the catalytic chemical vapor deposition approach. The intensity of the T-ZnO green emission can be modulated by topography and the proportion of carbon. Under proper experiment conditions, the carbon decorating leads to dramatically enhanced luminescence intensity of T-ZnO from 400 to 700 nm compared with no carbon decorated, which elevates this approach to a simple and effective method for the betterment of fluorescent materials in practical applications.

High-purity Cu nanocrystal synthesis by a dynamic decomposition method.

Cu nanocrystals are applied extensively in several fields, particularly in the microelectron, sensor, and catalysis. The catalytic behavior of Cu nanocrystals depends mainly on the structure and particle size. In this work, formation of high-purity Cu nanocrystals is studied using a common chemical vapor deposition precursor of cupric tartrate. This process is investigated through a combined experimental and computational approach. The decomposition kinetics is researched via differential scanning calorimetry and thermogravimetric analysis using Flynn-Wall-Ozawa, Kissinger, and Starink methods. The growth was found to be influenced by the factors of reaction temperature, protective gas, and time. And microstructural and thermal characterizations were performed by X-ray diffraction, scanning electron microscopy, transmission electron microscopy, and differential scanning calorimetry. Decomposition of cupric tartrate at different temperatures was simulated by density functional theory calculations under the generalized gradient approximation. High crystalline Cu nanocrystals without floccules were obtained from thermal decomposition of cupric tartrate at 271°C for 8 h under Ar. This general approach paves a way to controllable synthesis of Cu nanocrystals with high purity.

Water-filled balloon in the postoperative resection cavity improves dose distribution to target volumes in radiotherapy of maxillary sinus carcinoma.

Postoperative radiotherapy is a major treatment for patients with maxillary sinus carcinoma. However, the irregular resection cavity poses a technical difficulty for this treatment, causing uneven dose distribution to target volumes. In this study, we evaluated the dose distribution to target volumes and normal tissues in postoperative intensity-modulated radiotherapy (IMRT) after placing a water-filled balloon into the resection cavity. Three postoperative patients with advanced maxillary sinus carcinoma were selected in this trial. Water-filled balloons and supporting dental stents were fabricated according to the size of the maxillary resection cavity. Simulation CT scans were performed with or without water-filled balloons, IMRT treatment plans were established, and dose distribution to target volumes and organs at risk were evaluated. Compared to those in the treatment plan without balloons, the dose (D98) delivered to 98% of the gross tumor volume (GTV) increased by 2.1 Gy (P = 0.009), homogeneity index (HI) improved by 2.3% (P = 0.001), and target volume conformity index (TCI) of 68 Gy increased by 18.5% (P = 0.011) in the plan with balloons. Dosimetry endpoints of normal tissues around target regions in both plans were not significantly different (P > 0.05) except for the optic chiasm. In the plan without balloons, 68 Gy high-dose regions did not entirely cover target volumes in the ethmoid sinus, posteromedial wall of the maxillary sinus, or surgical margin of the hard palate. In contrast, 68 Gy high-dose regions entirely covered the GTV in the plan with balloons. These results suggest that placing a water-filled balloon in the resection cavity for postoperative IMRT of maxillary sinus carcinoma can reduce low-dose regions and markedly and simultaneously increase dose homogeneity and conformity of target volumes.

[Hair removal with intense pulsed light (IPL) for hairy skin grafts].

OBJECTIVE: To evaluate the hair-removal effect of IPL for hairy skin grafts. METHODS: 10 patients with hairy skin grafts were treated with IPL (ELITEplus) for hair removal 3 to 5 times at intervals of 2 months. The patients received IPL(610 - 1 000 nm) with spot size of 10 mm x 50 mm, pulse width of 5 msec with a delay of 2 - 20 msec, fluence of 30 - 38.3 J/cm2 . RESULTS: The treatment was well tolerated with no need of anesthesia in all the patients. The hairs were removed completely after 3 to 5 treatments. The treated site could be washed as usual with no need of dressing. There was no blister, infection, hyperpigmentation and scarring. Only vellus were existed during the follow-up period of 6 - 12 months. CONCLUSIONS: IPL is a safe and reliable method to remove unwanted hair on skin grafts. It can be easily performed with a lower complication.

[Hair removal with intense pulsed light].

OBJECTIVE: To evaluate the hair-removal effect of intense pulsed light (IPL). METHODS: The unwanted hairs were removed with ELITE plus IPL. The treatment took 3 - 5 procedures, with an interval of over 2 months. 341 patients underwent the treatment. RESULTS: All the patients were well tolerant without anesthesia. The unwanted hairs were wholly removed after 3 - 5 procedures. After operation, routine cleaning and make-up were allowed and bandaging was not necessary. There were blister in 3 cases and infection in 1 case. No pigmentation and scarring happened. Following-up of 3 - 6 months showed steady results with less regeneration of very thin and soft hair. CONCLUSIONS: IPL is an ideal hair-removal method because of the credible effect, simple operation, rapid treatment and no serious complications.