TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders

Enhancing the expressive capacity of deep learning-based time series models with self-supervised pre-training has become ever-increasingly prevalent in time series classification. Even though numerous efforts have been devoted to developing self-supervised models for time series data, we argue that the current methods are not sufficient to learn optimal time series representations due to solely unidirectional encoding over sparse point-wise input units. In this work, we propose TimeMAE, a novel self-supervised paradigm for learning transferrable time series representations based on transformer networks. The distinct characteristics of the TimeMAE lie in processing each time series into a sequence of non-overlapping sub-series via window-slicing partitioning, followed by random masking strategies over the semantic units of localized sub-series. Such a simple yet effective setting can help us achieve the goal of killing three birds with one stone, i.e., (1) learning enriched contextual representations of time series with a bidirectional encoding scheme; (2) increasing the information density of basic semantic units; (3) efficiently encoding representations of time series using transformer networks. Nevertheless, it is a non-trivial to perform reconstructing task over such a novel formulated modeling paradigm. To solve the discrepancy issue incurred by newly injected masked embeddings, we design a decoupled autoencoder architecture, which learns the representations of visible (unmasked) positions and masked ones with two different encoder modules, respectively. Furthermore, we construct two types of informative targets to accomplish the corresponding pretext tasks. One is to create a tokenizer module that assigns a codeword to each masked region, allowing the masked codeword classification (MCC) task to be completed effectively...Comment: Submitted to IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING(TKDE), under revie

Gene expression analysis of induced pluripotent stem cells from aneuploid chromosomal syndromes

BACKGROUND: Human aneuploidy is the leading cause of early pregnancy loss, mental retardation, and multiple congenital anomalies. Due to the high mortality associated with aneuploidy, the pathophysiological mechanisms of aneuploidy syndrome remain largely unknown. Previous studies focused mostly on whether dosage compensation occurs, and the next generation transcriptomics sequencing technology RNA-seq is expected to eventually uncover the mechanisms of gene expression regulation and the related pathological phenotypes in human aneuploidy. RESULTS: Using next generation transcriptomics sequencing technology RNA-seq, we profiled the transcriptomes of four human aneuploid induced pluripotent stem cell (iPSC) lines generated from monosomy × (Turner syndrome), trisomy 8 (Warkany syndrome 2), trisomy 13 (Patau syndrome), and partial trisomy 11:22 (Emanuel syndrome) as well as two umbilical cord matrix iPSC lines as euploid controls to examine how phenotypic abnormalities develop with aberrant karyotype. A total of 466 M (50-bp) reads were obtained from the six iPSC lines, and over 13,000 mRNAs were identified by gene annotation. Global analysis of gene expression profiles and functional analysis of differentially expressed (DE) genes were implemented. Over 5000 DE genes are determined between aneuploidy and euploid iPSCs respectively while 9 KEGG pathways are overlapped enriched in four aneuploidy samples. CONCLUSIONS: Our results demonstrate that the extra or missing chromosome has extensive effects on the whole transcriptome. Functional analysis of differentially expressed genes reveals that the genes most affected in aneuploid individuals are related to central nervous system development and tumorigenesis

Identification and validation of a novel cuproptosis-related gene signature in multiple myeloma

Background: Cuproptosis is a newly identified unique copper-triggered modality of mitochondrial cell death, distinct from known death mechanisms such as necroptosis, pyroptosis, and ferroptosis. Multiple myeloma (MM) is a hematologic neoplasm characterized by the malignant proliferation of plasma cells. In the development of MM, almost all patients undergo a relatively benign course from monoclonal gammopathy of undetermined significance (MGUS) to smoldering myeloma (SMM), which further progresses to active myeloma. However, the prognostic value of cuproptosis in MM remains unknown.Method: In this study, we systematically investigated the genetic variants, expression patterns, and prognostic value of cuproptosis-related genes (CRGs) in MM. CRG scores derived from the prognostic model were used to perform the risk stratification of MM patients. We then explored their differences in clinical characteristics and immune patterns and assessed their value in prognosis prediction and treatment response. Nomograms were also developed to improve predictive accuracy and clinical applicability. Finally, we collected MM cell lines and patient samples to validate marker gene expression by quantitative real-time PCR (qRT-PCR).Results: The evolution from MGUS and SMM to MM was also accompanied by differences in the CRG expression profile. Then, a well-performing cuproptosis-related risk model was developed to predict prognosis in MM and was validated in two external cohorts. The high-risk group exhibited higher clinical risk indicators. Cox regression analyses showed that the model was an independent prognostic predictor in MM. Patients in the high-risk group had significantly lower survival rates than those in the low-risk group (p < 0.001). Meanwhile, CRG scores were significantly correlated with immune infiltration, stemness index and immunotherapy sensitivity. We further revealed the close association between CRG scores and mitochondrial metabolism. Subsequently, the prediction nomogram showed good predictive power and calibration. Finally, the prognostic CRGs were further validated by qRT-PCR in vitro.Conclusion: CRGs were closely related to the immune pattern and self-renewal biology of cancer cells in MM. This prognostic model provided a new perspective for the risk stratification and treatment response prediction of MM patients

A novel glycolysis-related gene signature for predicting the prognosis of multiple myeloma

Background: Metabolic reprogramming is an important hallmark of cancer. Glycolysis provides the conditions on which multiple myeloma (MM) thrives. Due to MM’s great heterogeneity and incurability, risk assessment and treatment choices are still difficult.Method: We constructed a glycolysis-related prognostic model by Least absolute shrinkage and selection operator (LASSO) Cox regression analysis. It was validated in two independent external cohorts, cell lines, and our clinical specimens. The model was also explored for its biological properties, immune microenvironment, and therapeutic response including immunotherapy. Finally, multiple metrics were combined to construct a nomogram to assist in personalized prediction of survival outcomes.Results: A wide range of variants and heterogeneous expression profiles of glycolysis-related genes were observed in MM. The prognostic model behaved well in differentiating between populations with various prognoses and proved to be an independent prognostic factor. This prognostic signature closely coordinated with multiple malignant features such as high-risk clinical features, immune dysfunction, stem cell-like features, cancer-related pathways, which was associated with the survival outcomes of MM. In terms of treatment, the high-risk group showed resistance to conventional drugs such as bortezomib, doxorubicin and immunotherapy. The joint scores generated by the nomogram showed higher clinical benefit than other clinical indicators. The in vitro experiments with cell lines and clinical subjects further provided convincing evidence for our study.Conclusion: We developed and validated the utility of the MM glycolysis-related prognostic model, which provides a new direction for prognosis assessment, treatment options for MM patients

In Situ Sputtering Silver Induction Electrode for Stable and Stretchable Triboelectric Nanogenerators

Triboelectric nanogenerators (TENG) can convert mechanical energy into electricity and exhibit unique advantages in the field of low-frequency and discrete energy harvesting. However, the interfacial state and stability between the triboelectric layer and electrode layer influence the output and applications of TENG. Herein, an in situ sputtering Ag process for fabricating induction electrodes is proposed to match with TENG. The sputtering Ag process is optimized by a variety of parameters, such as sputtering power, single-cycle time, number of cycles, cycle interval, and vacuum degree. In addition, the chemical state of Ag as a function of air placement is investigated, showing the sputtered Ag has excellent conductivity and stability. Moreover, four kinds of polymers are selected for fabricating TENGs based on the sputtered Ag induction electrodes, i.e., nylon 66, polyimide (PI), fluorinated ethylene propylene (FEP), and polydimethylsiloxane (PDMS), which shows great applicability. Considering the demand of flexible power suppliers, the sputtered Ag is integrated with a PDMS substrate, and shows good adhesion, flexibility, and ductility after severe deformation of the PDMS. Finally, the developed induction electrode processing technology is used in flexible TENG and shows great prospects in self-powered electronics for practical applications

A Miniature Four-Channel Ion Trap Array Based on Non-silicon MEMS Technology

With the increasing application field, a higher requirement is put forward for the mass spectrometer. The reduction in size will inevitably cause a loss of precision; therefore, it is necessary to develop a high-performance miniature mass spectrometer. Based on the researches of rectangular ion trap, the relationship between mass resolution and structural parameters of the ion trap array was analyzed by further simulation. The results indicate that, considering the balance of mass resolution and extraction efficiency, the preferable values for the field radius of exit direction y0 and ion exit slot width s0 are 1.61 mm and 200 μm, respectively. Afterwards, a miniature four-channel ion trap array (MFITA) was fabricated, by using MEMS and laser etching technology, and mass spectrometry experiments were carried out to demonstrate its performance. The mass resolution of butyl diacetate with m/z = 230 can reach 324. In addition, the consistency of four channels is verified within the error tolerance, by analyzing air samples. Our work can prove the correctness of the structural design and the feasibility of MEMS preparation for MFITA, which will bring meaningful guidance for its future development and optimization

ZiBuPiYin recipe protects db/db mice from diabetes-associated cognitive decline through improving multiple pathological changes.

Multiple organ systems, including the brain, which undergoes changes that may increase the risk of cognitive decline, are adversely affected by diabetes mellitus (DM). Here, we demonstrate that type 2 diabetes mellitus (T2DM) db/db mice exhibited hippocampus-dependent memory impairment, which might associate with a reduction in dendritic spine density in the pyramidal neurons of brain, Aβ1-42 deposition in the prefrontal cortex (PFC) and hippocampus, and a decreased expression of neurostructural proteins including microtubule-associated protein (MAP2), a marker of dendrites, and postsynaptic density 95 (PSD95), a marker of excitatory synapses. To investigate the effects of the ZiBuPiYin recipe (ZBPYR), a traditional Chinese medicine recipe, on diabetes-related cognitive decline (DACD), db/db mice received daily administration of ZBPYR over an experimental period of 6 weeks. We then confirmed that ZBPYR rescued learning and memory performance impairments, reversed dendritic spine loss, reduced Aβ1-42 deposition and restored the expression levels of MAP2 and PSD95. The present study also revealed that ZBPYR strengthened brain leptin and insulin signaling and inhibited GSK3β overactivity, which may be the potential mechanism or underlying targets of ZBPYR. These findings conclude that ZBPYR prevents DACD, most likely by improving dendritic spine density and attenuating brain leptin and insulin signaling pathway injury. Our findings provide further evidence for the effects of ZBPYR on DACD

Simulation of a Miniature Linear Ion Trap with Half-Round Rod Electrodes

The miniaturization of ion trap mass analyzers is an important direction in the development of mass spectrometers. In this work, we proposed two models of miniaturized HreLIT with a field radius of about 2 mm based on the existing research on conventional HreLIT and other ion traps, one with ions ejection slits on one pair of electrodes only (2-slit model) and the other with the same slits on all electrodes (4-slit model). The relationship of mass resolution with r/rx and the “stretch” distance of electrodes in the ejection direction is investigated by theoretical simulations. Trends of electric fields inside the ion traps were discussed as well. The comparable maximum resolution is observed at r/rx = 2/1.4 in both models, but stretching simulations revealed that the peak resolution of the 2-slit model was higher than that of the other model by about 8%. The highest value of 517 was obtained when stretching 1.1 mm. Furthermore, the resolution of ions with m/z = 119 could exceed 1000 when the scan rate was reduced to 800 Th/s. The mass spectrometry capability of miniature HreLIT has been confirmed theoretically, and it laid the foundation for the subsequent fabrication with MEMS technology

Advantage of Next-Generation Sequencing in Dynamic Monitoring of Circulating Tumor DNA over Droplet Digital PCR in Cetuximab Treated Colorectal Cancer Patients

Epidermal growth factor receptor (EGFR) blockade resistance is common in the treatment of RAS wide type colorectal cancer (CRC). During the treatment of cetuximab, acquired resistant genomic alterations always occurs earlier than disease progression observed by medical images. Identification of genomic alterations dynamically might have certain clinical significance. Because of the limitation of repeated tissue biopsy, liquid biopsy is increasingly recognized. Droplet digital polymerase chain reaction (ddPCR) is the main detection methods for circulating tumor DNA (ctDNA), however, the application of next-generation sequencing (NGS) for ctDNA detection becomes more and more popular. Here we develop a NGS-based ctDNA assay and evaluated its sensitivity and specificity while using ddPCR as control. These two technologies were both used for genomic alteration detection for the peripheral blood samples from cetuximab-treated colorectal cancer patients dynamically. Fifteen patients were enrolled in this study, including eight males and seven females. The sensitivity and specificity of our NGS assay were 87.5% and 100% respectively, and liner regression analysis comparing variant allele frequency (VAF) revealed high concordance between NGS and ddPCR (R2 = 0.98). NGS actually found more mutation information than ddPCR such as the additional dynamic changes of TP53 which were observed in the disease progression patients. Moreover, the variant allele fraction of TP53 was also found by NGS to be changed along with the clinical efficacy evaluation dynamically during the whole treatment process. In conclusion, our newly developed NGS-based ctDNA assay shows similar performance with ddPCR but have more advantages of its high throughput of multigenetic detection for the dynamic monitoring during the treatment of cetuximab in metastasis CRC patients