Interplay between multiple charge-density waves and the relationship with superconductivity in Pdx_xHoTe3_{3}

HoTe$_{3}$, a member of the rare-earth tritelluride ($R$Te$_{3}$) family, and its Pd-intercalated compounds, Pd$_x$HoTe$_{3}$, where superconductivity (SC) sets in as the charge-density wave (CDW) transition is suppressed by the intercalation of a small amount of Pd, are investigated using angle-resolved photoemission spectroscopy (ARPES) and electrical resistivity. Two incommensurate CDWs with perpendicular nesting vectors are observed in HoTe$_{3}$ at low temperatures. With a slight Pd intercalation ($x$ = 0.01), the large CDW gap decreases and the small one increases. The momentum dependence of the gaps along the inner Fermi surface (FS) evolves from orthorhombicity to near tetragonality, manifesting the competition between two CDW orders. At $x$ = 0.02, both CDW gaps decreases with the emergence of SC. Further increasing the content of Pd for $x$ = 0.04 will completely suppress the CDW instabilities and give rise to the maximal SC order. The evolution of the electronic structures and electron-phonon couplings (EPCs) of the multiple CDWs upon Pd intercalation are carefully scrutinized. We discuss the interplay between multiple CDW orders, and the competition between CDW and SC in detail.Comment: 6 pages, 5 figure

Detecting somatisation disorder via speech: introducing the Shenzhen Somatisation Speech Corpus

Objective Speech recognition technology is widely used as a mature technical approach in many fields. In the study of depression recognition, speech signals are commonly used due to their convenience and ease of acquisition. Though speech recognition is popular in the research field of depression recognition, it has been little studied in somatisation disorder recognition. The reason for this is the lack of a publicly accessible database of relevant speech and benchmark studies. To this end, we introduce our somatisation disorder speech database and give benchmark results. Methods By collecting speech samples of somatisation disorder patients, in cooperation with the Shenzhen University General Hospital, we introduce our somatisation disorder speech database, the Shenzhen Somatisation Speech Corpus (SSSC). Moreover, a benchmark for SSSC using classic acoustic features and a machine learning model is proposed in our work. Results To obtain a more scientific benchmark, we have compared and analysed the performance of different acoustic features, i. e., the full ComParE feature set, or only MFCCs, fundamental frequency (F0), and frequency and bandwidth of the formants (F1-F3). By comparison. the best result of our benchmark is the 76.0 % unweighted average recall achieved by a support vector machine with formants F1–F3. Conclusion The proposal of SSSC bridges a research gap in somatisation disorder, providing researchers with a publicly accessible speech database. In addition, the results of the benchmark show the scientific validity and feasibility of computer audition for speech recognition in somatization disorders

Battling with the low-resource condition for snore sound recognition: introducing a meta-learning strategy

Snoring affects 57 % of men, 40 % of women, and 27 % of children in the USA. Besides, snoring is highly correlated with obstructive sleep apnoea (OSA), which is characterised by loud and frequent snoring. OSA is also closely associated with various life-threatening diseases such as sudden cardiac arrest and is regarded as a grave medical ailment. Preliminary studies have shown that in the USA, OSA affects over 34 % of men and 14 % of women. In recent years, polysomnography has increasingly been used to diagnose OSA. However, due to its drawbacks such as being time-consuming and costly, intelligent audio analysis of snoring has emerged as an alternative method. Considering the higher demand for identifying the excitation location of snoring in clinical practice, we utilised the Munich-Passau Snore Sound Corpus (MPSSC) snoring database which classifies the snoring excitation location into four categories. Nonetheless, the problem of small samples remains in the MPSSC database due to factors such as privacy concerns and difficulties in accurate labelling. In fact, accurately labelled medical data that can be used for machine learning is often scarce, especially for rare diseases. In view of this, Model-Agnostic Meta-Learning (MAML), a small sample method based on meta-learning, is used to classify snore signals with less resources in this work. The experimental results indicate that even when using only the ESC-50 dataset (non-snoring sound signals) as the data for meta-training, we are able to achieve an unweighted average recall of 60.2 % on the test dataset after fine-tuning on just 36 instances of snoring from the development part of the MPSSC dataset. While our results only exceed the baseline by 4.4 %, they still demonstrate that even with fine-tuning on a few instances of snoring, our model can outperform the baseline. This implies that the MAML algorithm can effectively tackle the low-resource problem even with limited data resources

Observation of electronic nematicity driven by three-dimensional charge density wave in kagome lattice KV3_3Sb5_5

Kagome superconductors AV$_3$Sb$_5$ (A = K, Rb, Cs) provide a fertile playground for studying various intriguing phenomena such as non-trivial band topology, superconductivity, giant anomalous Hall effect, and charge density wave (CDW). Remarkably, the recent discovery of $C_2$ symmetric nematic phase prior to the superconducting state in AV$_3$Sb$_5$ has drawn enormous attention, as the unusual superconductivity might inherit the symmetry of the nematic phase. Although many efforts have been devoted to resolve the charge orders using real-space microscopy and transport measurements, the direct evidence on the rotation symmetry breaking of the electronic structure in the CDW state from the reciprocal space is still rare. The underlying mechanism is still ambiguous. Here, utilizing the micron-scale spatially resolved angle-resolved photoemission spectroscopy, we observed the fingerprint of band folding in the CDW phase of KV$_3$Sb$_5$, which yet demonstrates the unconventional unidirectionality, and is indicative of the rotation symmetry breaking from $C_6$ to $C_2$. We then pinpointed that the interlayer coupling between adjacent planes with $\pi$-phase offset in the 2$\times$2$\times$2 CDW phase would lead to the preferred twofold symmetric electronic structure. Time-reversal symmetry is further broken at temperatures below $\sim$ 40 K as characterized by giant anomalous Hall effect triggered by weak magnetic fields. These rarely observed unidirectional back-folded bands with time-reversal symmetry breaking in KV$_3$Sb$_5$ may provide important insights into its peculiar charge order and superconductivity

Who can help me? Understanding the antecedent and consequence of medical information seeking behavior in the era of bigdata

IntroductionThe advent of bigdata era fundamentally transformed the nature of medical information seeking and the traditional binary medical relationship. Weaving stress coping theory and information processing theory, we developed an integrative perspective on information seeking behavior and explored the antecedent and consequence of such behavior.MethodsData were collected from 573 women suffering from infertility who was seeking assisted reproductive technology treatment in China. We used AMOS 22.0 and the PROCESS macro in SPSS 25.0 software to test our model.ResultsOur findings demonstrated that patients’ satisfaction with information received from the physicians negatively predicted their behavior involvement in information seeking, such behavior positively related to their perceived information overload, and the latter negatively related to patient-physician relationship quality. Further findings showed that medical information seeking behavior and perceived information overload would serially mediate the impacts of satisfaction with information received from physicians on patient-physician relationship quality.DiscussionThis study extends knowledge of information seeking behavior by proposing an integrative model and expands the application of stress coping theory and information processing theory. Additionally, it provides valuable implications for patients, physicians and public health information service providers

A Multi-Task Dense Network with Self-Supervised Learning for Retinal Vessel Segmentation

Morphological and functional changes in retinal vessels are indicators of a variety of chronic diseases, such as diabetes, stroke, and hypertension. However, without a large number of high-quality annotations, existing deep learning-based medical image segmentation approaches may degrade their performance dramatically on the retinal vessel segmentation task. To reduce the demand of high-quality annotations and make full use of massive unlabeled data, we propose a self-supervised multi-task strategy to extract curvilinear vessel features for the retinal vessel segmentation task. Specifically, we use a dense network to extract more vessel features across different layers/slices, which is elaborately designed for hardware to train and test efficiently. Then, we combine three general pre-training tasks (i.e., intensity transformation, random pixel filling, in-painting and out-painting) in an aggregated way to learn rich hierarchical representations of curvilinear retinal vessel structures. Furthermore, a vector classification task module is introduced as another pre-training task to obtain more spatial features. Finally, to make the segmentation network pay more attention to curvilinear structures, a novel dynamic loss is proposed to learn robust vessel details from unlabeled fundus images. These four pre-training tasks greatly reduce the reliance on labeled data. Moreover, our network can learn the retinal vessel features effectively in the pre-training process, which leads to better performance in the target multi-modal segmentation task. Experimental results show that our method provides a promising direction for the retinal vessel segmentation task. Compared with other state-of-the-art supervised deep learning-based methods applied, our method requires less labeled data and achieves comparable segmentation accuracy. For instance, we match the accuracy of the traditional supervised learning methods on DRIVE and Vampire datasets without needing any labeled ground truth image. With elaborately training, we gain the 0.96 accuracy on DRIVE dataset

Identification of four prognostic LncRNAs for survival prediction of patients with hepatocellular carcinoma

Background As the fifth most common cancer worldwide, Hepatocellular carcinoma (HCC) is also the third most common cause of cancer-related death in China. Several lncRNAs have been demonstrated to be associated with occurrence and prognosis of HCC. However, identification of prognostic lncRNA signature for HCC with expression profiling data has not been conducted yet. Methods With the reuse of public available TCGA data, expression profiles of lncRNA for 371 patients with HCC were obtained and analyzed to find the independent prognostic lncRNA. Based on the expression of lncRNA, we developed a risk score model, which was evaluated by survival analysis and ROC (receiver operating characteristic) curve. Enrichment analysis was performed to predict the possible role of the identified lncRNA in HCC prognosis. Results Four lncRNAs (RP11-322E11.5, RP11-150O12.3, AC093609.1, CTC-297N7.9) were found to be significantly and independently associated with survival of HCC patients. We used these four lncRNAs to construct a risk score model, which exhibited a strong ability to distinguish patients with significantly different prognosis (HR = 2.718, 95% CI [2.103–3.514], p = 2.32e−14). Similar results were observed in the subsequent stratification survival analysis for HBV infection status and pathological stage. The ROC curve also implied our risk score as a good indicator for 5-year survival prediction. Furthermore, enrichment analysis revealed that the four signature lncRNAs may be involved in multiple pathways related to tumorigenesis and prognosis. Discussion Our study recognized four lncRNAs to be significantly associated with prognosis of liver cancer, and could provide novel insights into the potential mechanisms of HCC progression. Additionally, CTC-297N7.9 may influence the downstream TMEM220 gene expression through cis-regualtion. Nevertheless, further well-designed experimental studies are needed to validate our findings