Biomarkers for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer deaths worldwide. The HCC diagnosis is usually achieved by biomarkers, which can also help in prognosis prediction. Furthermore, it might represent certain therapeutic interventions through some combinations of biomarkers. Here, we review on our current understanding of HCC biomarkers

Accurate segmentation algorithm of acoustic neuroma in the cerebellopontine angle based on ACP-TransUNet

Acoustic neuroma is one of the most common tumors in the cerebellopontine angle area. Patients with acoustic neuroma have clinical manifestations of the cerebellopontine angle occupying syndrome, such as tinnitus, hearing impairment and even hearing loss. Acoustic neuromas often grow in the internal auditory canal. Neurosurgeons need to observe the lesion contour with the help of MRI images, which not only takes a lot of time, but also is easily affected by subjective factors. Therefore, the automatic and accurate segmentation of acoustic neuroma in cerebellopontine angle on MRI is of great significance for surgical treatment and expected rehabilitation. In this paper, an automatic segmentation method based on Transformer is proposed, using TransUNet as the core model. As some acoustic neuromas are irregular in shape and grow into the internal auditory canal, larger receptive fields are thus needed to synthesize the features. Therefore, we added Atrous Spatial Pyramid Pooling to CNN, which can obtain a larger receptive field without losing too much resolution. Since acoustic neuromas often occur in the cerebellopontine angle area with relatively fixed position, we combined channel attention with pixel attention in the up-sampling stage so as to make our model automatically learn different weights by adding the attention mechanism. In addition, we collected 300 MRI sequence nuclear resonance images of patients with acoustic neuromas in Tianjin Huanhu hospital for training and verification. The ablation experimental results show that the proposed method is reasonable and effective. The comparative experimental results show that the Dice and Hausdorff 95 metrics of the proposed method reach 95.74% and 1.9476 mm respectively, indicating that it is not only superior to the classical models such as UNet, PANet, PSPNet, UNet++, and DeepLabv3, but also show better performance than the newly-proposed SOTA (state-of-the-art) models such as CCNet, MANet, BiseNetv2, Swin-Unet, MedT, TransUNet, and UCTransNet

A perspective on energy chemistry of low-temperature lithium metal batteries

Dendrite growth of lithium (Li) metal anode severely hinders its practical application, while the situation becomes more serious at low temperatures due to the sluggish kinetics of Li-ion diffusion. This perspective is intended to clearly understand the energy chemistry of low-temperature Li metal batteries (LMBs). The low-temperature chemistries between LMBs and traditional Li-ion batteries are firstly compared to figure out the features of the low-temperature LMBs. Li deposition behaviors at low temperatures are then discussed concerning the variation in Li-ion diffusion behaviors and solid electrolyte interphase (SEI) features. Subsequently, the strategies to enhance the diffusion kinetics of Li ions and suppress dendrite growth including designing electrolytes and electrode/electrolyte interfaces are analyzed. Finally, conclusions and outlooks are drawn to shed lights on the future design of high-performance low-temperature LMBs

Multidimensional signals and analytic flexibility: Estimating degrees of freedom in human speech analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis which can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling, but also from decisions regarding the quantification of the measured behavior. In the present study, we gave the same speech production data set to 46 teams of researchers and asked them to answer the same research question, resulting insubstantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further find little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system and calibrate their (un)certainty in their conclusions

Progress in Biological Functions of NBAS Gene and Related Diseases

Neuroblastoma-amplified sequence (NBAS) is a highly conserved gene firstly found in neuroblastoma, targeting on the human chromosome 2 p24.3 and encodes a protein that is a subunit of the Syntaxin 18 complex. The functions of NBAS include the involvement in transport of Golgi-to-Endoplasmic Reticulum retrograde and degradation of nonsense-mediated mRNA. NBAS gene is widely expressed in more than 30 tissues, suggesting that it may play an important role in human body. In 2010 and 2015, NBAS was identified successively as the pathogenic gene of SOPH syndrome and fever-related liver failure respectively. Recent studies shows that NBAS gene mutations can involve immune system and cause immune deficiency. In this paper, we review the biological function of NBAS, NBAS gene mutation-related diseases and their pathogenesis in recent years

The Structure of Flavor Mixing and Reconstruction of the Mass Matrix

The Fermion flavor structure is investigated by bilinear decomposition of the mass matrix after EW symmetry breaking, and the roles of factorized matrices in flavor mixing and mass generation are explored. It is shown that flavor mixing can be addressed as an independent issue. On a new Yukawa basis, the minimal parameterization of flavor mixing is realized containing two relative phases and two free $SO(2)_L$ rotation angles. The validity of the flavor mixing structure is checked in both the lepton and quark sectors. Under the decomposition of flavor mixing, fermion mass matrices are reconstructed under the hierarchy limit. A flat mass matrix with all elements equal to 1 arises naturally from the requirement that homology exists between up-type and down-type fermion mass matrices. Some hints of a flat matrix and flavor breaking are also discussed.Comment: 12 pages, 1 figure

Spectral Properties of Hypergraph Laplacian and Approximation Algorithms

The celebrated Cheeger's Inequality (Alon and Milman 1985; Alon 1986) establishes a bound on the edge expansion of a graph via its spectrum. This inequality is central to a rich spectral theory of graphs, based on studying the eigenvalues and eigenvectors of the adjacency matrix (and other related matrices) of graphs. It has remained open to define a suitable spectral model for hypergraphs whose spectra can be used to estimate various combinatorial properties of the hypergraph. In this article, we introduce a new hypergraph Laplacian operator generalizing the Laplacian matrix of graphs. In particular, the operator is induced by a diffusion process on the hypergraph, such that within each hyperedge, measure flows from vertices having maximum weighted measure to those having minimum. Since the operator is nonlinear, we have to exploit other properties of the diffusion process to recover the Cheeger's Inequality that relates hyperedge expansion with the ``second eigenvalue'' of the resulting Laplacian. However, we show that higher-order spectral properties cannot hold in general using the current framework. Since higher-order spectral properties do not hold for the Laplacian operator, we instead use the concept of procedural minimizers to consider higher-order Cheeger-like inequalities. For any k is an element of N, we give a polynomial-time algorithm to compute an O(log r)-approximation to the kth procedural minimizer, where r is the maximum cardinality of a hyperedge. We show that this approximation factor is optimal under the SSE hypothesis (introduced by Raghavendra and Steurer (2010)) for constant values of k. Moreover, using the factor-preserving reduction from vertex expansion in graphs to hypergraph expansion, we show that all our results for hypergraphs extend to vertex expansion in graphs