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The carbon catabolite repression 4-negative on TATA-less transcription complex subunit 3 gene ( CONT3 ) plays a key role in regulating the mRNA transcription and protein translation of other genes. Mutations in CONT3 have also recently been implicated as a causative factor of intellectual developmental disorder with speech delay, autism, and dysmorphic facies (IDDSADF). However, to date, only a few CONT3 mutations have been reported to be associated with IDDSADF-related diseases. In the present case, we report a Chinese patient with developmental delay, verbal regression, and facial dysmorphism, in whom cerebral magnetic resonance imaging showed an expansion of the lateral ventricle. The patient was diagnosed with an IDDSADF-related disease caused by a de novo c.1616_1623del mutation in exon 14 of CONT3 , which was confirmed by whole-exome sequencing and direct Sanger sequencing. This case report is the first known documentation of a pathogenic mutation at the c.1616_1623del locus of CONT3 in the worldwide population. It provides a critical theoretical basis for the specific gene-based diagnosis of IDDSADF-related diseases and expands the mutation profile of CONT3 .

The density analysis of woven fabrics is a critical part of quality control in textile production. Traditional image-processing-based methods for density analysis of woven fabrics require complicated manual feature design and lack adaptability to different weaving patterns. To address these problems, we propose a woven fabric density analysis method based on small object detection and rule-based post-processing. First, we capture high-resolution images of woven fabrics using macro-microscopic camera equipment, and then construct a woven fabric microscopic image dataset for our study through pre-processing and data augmentation. Next, we propose a multitask feature fusion network (MTF-Net), a small object detection network, to detect the float-points of warp and weft yarns. The detection ability of the model is improved by the cooperation of a reconstruction branch network, a pixel-level branch network, and an object-level branch network. Additionally, we introduce a feature rotation selection module (FRSM) to solve the problem of yarns with small angle rotations. We finally propose a rule-based post-processing method to complete the density analysis of woven fabrics. The experimental results demonstrate that the proposed method is effective and achieves higher accuracy than the popular object detection methods for density analysis on the constructed woven fabric dataset.

Brain–computer interfaces have seen extraordinary surges in developments in recent years, and a significant discrepancy now exists between the abundance of available data and the limited headway made in achieving a unified theoretical framework. This discrepancy becomes particularly pronounced when examining the collective neural activity at the micro and meso scale, where a coherent formalization that adequately describes neural interactions is still lacking. Here, we introduce a mathematical framework to analyze systems of natural neurons and interpret the related empirical observations in terms of lattice field theory, an established paradigm from theoretical particle physics and statistical mechanics. Our methods are tailored to interpret data from chronic neural interfaces, especially spike rasters from measurements of single neuron activity, and generalize the maximum entropy model for neural networks so that the time evolution of the system is also taken into account. This is obtained by bridging particle physics and neuroscience, paving the way for particle physics-inspired models of the neocortex.

Graph neural networks (GNNs) excel in modeling relational data such as biological, social, and transportation networks, but the underpinnings of their success are not well understood. Traditional complexity measures from statistical learning theory fail to account for observed phenomena like the double descent or the impact of relational semantics on generalization error. Motivated by experimental observations of "transductive" double descent in key networks and datasets, we use analytical tools from statistical physics and random matrix theory to precisely characterize generalization in simple graph convolution networks on the contextual stochastic block model. Our results illuminate the nuances of learning on homophilic versus heterophilic data and predict double descent whose existence in GNNs has been questioned by recent work. We show how risk is shaped by the interplay between the graph noise, feature noise, and the number of training labels. Our findings apply beyond stylized models, capturing qualitative trends in real-world GNNs and datasets. As a case in point, we use our analytic insights to improve performance of state-of-the-art graph convolution networks on heterophilic datasets. [ABSTRACT FROM AUTHOR]