SSHNN: Semi-Supervised Hybrid NAS Network for Echocardiographic Image Segmentation

Accurate medical image segmentation especially for echocardiographic images with unmissable noise requires elaborate network design. Compared with manual design, Neural Architecture Search (NAS) realizes better segmentation results due to larger search space and automatic optimization, but most of the existing methods are weak in layer-wise feature aggregation and adopt a ``strong encoder, weak decoder" structure, insufficient to handle global relationships and local details. To resolve these issues, we propose a novel semi-supervised hybrid NAS network for accurate medical image segmentation termed SSHNN. In SSHNN, we creatively use convolution operation in layer-wise feature fusion instead of normalized scalars to avoid losing details, making NAS a stronger encoder. Moreover, Transformers are introduced for the compensation of global context and U-shaped decoder is designed to efficiently connect global context with local features. Specifically, we implement a semi-supervised algorithm Mean-Teacher to overcome the limited volume problem of labeled medical image dataset. Extensive experiments on CAMUS echocardiography dataset demonstrate that SSHNN outperforms state-of-the-art approaches and realizes accurate segmentation. Code will be made publicly available.Comment: Submitted to ICASSP202

Investigating the shared genetic architecture between hypothyroidism and rheumatoid arthritis

BackgroundThere is still controversy regarding the relationship between hypothyroidism and rheumatoid arthritis (RA), and there has been a dearth of studies on this association. The purpose of our study was to explore the shared genetic architecture between hypothyroidism and RA.MethodsUsing public genome-wide association studies summary statistics of hypothyroidism and RA, we explored shared genetics between hypothyroidism and RA using linkage disequilibrium score regression, ρ-HESS, Pleiotropic analysis under a composite null hypothesis (PLACO), colocalization analysis, Multi-Trait Analysis of GWAS (MTAG), and transcriptome-wide association study (TWAS), and investigated causal associations using Mendelian randomization (MR).ResultsWe found a positive genetic association between hypothyroidism and RA, particularly in local genomic regions. Mendelian randomization analysis suggested a potential causal association of hypothyroidism with RA. Incorporating gene expression data, we observed that the genetic associations between hypothyroidism and RA were enriched in various tissues, including the spleen, lung, small intestine, adipose visceral, and blood. A comprehensive approach integrating PLACO, Bayesian colocalization analysis, MTAG, and TWAS, we successfully identified TYK2, IL2RA, and IRF5 as shared risk genes for both hypothyroidism and RA.ConclusionsOur investigation unveiled a shared genetic architecture between these two diseases, providing novel insights into the underlying biological mechanisms and establishing a foundation for more effective interventions

GDF15 Regulates Malat-1 Circular RNA and Inactivates NFκB Signaling Leading to Immune Tolerogenic DCs for Preventing Alloimmune Rejection in Heart Transplantation

Recombinant human growth differentiation factor 15 (rhGDF15) affects dendritic cell (DC) maturation. However, whether GDF15 is expressed in DCs and its roles and signaling in DCs remain largely unknown. It is unclear whether GDF15-DCs can induce immune tolerance in heart transplantation (HT). This study aims to understand the impact of endogenous GDF15 on DC's development, function, underlying molecular mechanism including circular RNA (circRNA). This study will also explore GDF15-DC-mediated immune modulation in HT. Bone marrow (BM) derived DCs were cultured and treated to up- or down regulate GDF15 expression. Phenotype and function of DCs were detected. Expression of genes and circRNAs was determined by qRT-PCR. The signaling pathways activated by GDF15 were examined. The impact of GDF15 treated DCs on preventing allograft immune rejection was assessed in a MHC full mismatch mouse HT model. Our results showed that GDF15 was expressed in DCs. Knockout of GDF15 promoted DC maturation, enhanced immune responsive functions, up-regulated malat-1 circular RNA (circ_Malat 1), and activated the nuclear factor kappa B (NFκB) pathway. Overexpression of GDF15 in DCs increased immunosuppressive/inhibitory molecules, enhanced DCs to induce T cell exhaustion, and promoted Treg generation through IDO signaling. GDF15 utilized transforming growth factor (TGF) β receptors I and II, not GFAL. Administration of GDF15 treated DCs prevented allograft rejection and induced immune tolerance in transplantation. In conclusion, GDF15 induces tolerogenic DCs (Tol-DCs) through inhibition of circ_Malat-1 and the NFκB signaling pathway and up-regulation of IDO. GDF15-DCs can prevent alloimmune rejection in HT

An overview of hybrid systems consisting of synchronous condensers and battery energy storage system to support power grid

As wind farms are integrated into power systems, synchronous condensers (SCs) could play an important role in support of reactive power, inertia, short-circuit current, and working as a voltage source. In practical projects and various places, the deployment of SC has verified its functions, and the stability of the power system has been improved. Battery energy storage system (BESS), as a relatively mature energy storage technology, can bring frequency support to the power system. Therefore, it also retains the advantages of power electronics, providing reactive power and a fast response to the power system. In this paper, we review the development of both techniques and discuss the future feasibility of hybrid systems. In addition to analyzing the existing hybrid systems of SC and BESS in parallel, we also propose a novel type of hybrid system, which may be one of the development directions of hybrid systems and renewable energy power systems

Co-ordinated grid forming control of AC-side-connected energy storage systems for converter-interfaced generation

Grid forming control of converter interfaced generation (CIG) requires some form of energy storage to be coupled with the generation. Energy storage systems (ESSs) can be coupled to the CIG either on the DC or the AC side of the power converter. When placed on the DC side, the ESS can provide damping of the variability in the generation but would require significant modification to the wind turbine hardware. The solution with an ESS connected to the AC side is simpler to implement with existing wind turbines but fails to provide damping of the CIG generation. This paper proposes a grid forming control strategy, based on virtual synchronous generator (VSG) control, which allows the ESS installed at the AC-side of the converter to have the same features and dynamic behaviour as those obtained from placement on the DC-side of the converter. In addition, the proposed control can also limit the exchanged power of the ESS within its rating for a safe operation. The proposed control is validated via a detailed Electro-Magnetic Transient (EMT) model and its impact on the grid is quantified via the case study of the All-Island Irish transmission system. Simulation results show that only a small ESS capacity can ensure that the frequency variance satisfies the grid code requirement even in the situation of a very high CIG penetration

A Novel Nested Polymerase Chain Reaction (n-PCR) Assay for Identifying Sorghum nitidum

This work developed a novel nested polymerase chain reaction (n-PCR) assay to identify Sorghum nitidum (S. nitidum). It has been designed a set of specific n-PCR inner primers Snit5/Snit2 and outer primers Nout1/Nout2 based on a conserved nucleotide sequence of adh1-like gene of S. nitidum. Fourteen samples of sorghum were used to investigate the specificity of the primers and the n-PCR assay. The result showed that 9 samples of S. nitidum displayed a positive strong, specific amplified band at ~873 bp in gel spectra, while other relatives, including Sorghum halepense, Sorghum almum, Sorghum bicolor, Sorghum propinum and Sorghum sudanse exhibited negative amplifications. This assay was able to specifically identify S. nitidum fast and effectively, which could be applied widely in field inspection, agriculture production and plant protection

RAPD Marker Conversion into a SCAR Marker for Rapid Identification of Johnsongrass [Sorghum halepense (L.) Pers.]

Johnsongrass [Sorghum halepense (L.) Pers.] is a malignant weed in the world, threatening biodiversity at invaded habitats in more than 50 countries. Because of similarity in morphological characters, S. halepense and its relatives, S. almum, S. nitidum, S. propinquum, S. sudanense, and S. bicolor, etc. was difficult to identify. As a supplementary methodolgy for morphology identification, a molecular detection method was established. Sequence Characterized Amplified Regions (SCAR) marker is a recent established, reliabile, and stable molecular marker based on RAPD maker, an effective way for germplasm identification. In this study, one specific band of S.halepense was screened by 163 pairs of RAPD primers. According to the sequences of the band, a pair of special SCAR primers SH1/SH2 was designed and verified by 65 Sorghum DNA samples from all over the world. The results showed SCAR primers SH1/SH2 can be used to distinguish S.halepense and its relatives rapidly with three exceptions of Australia geotypes.</p

Identification of distinct clinical phenotypes of cardiogenic shock using machine learning consensus clustering approach

Abstract Background Cardiogenic shock (CS) is a complex state with many underlying causes and associated outcomes. It is still difficult to differentiate between various CS phenotypes. We investigated if the CS phenotypes with distinctive clinical profiles and prognoses might be found using the machine learning (ML) consensus clustering approach. Methods The current study included patients who were diagnosed with CS at the time of admission from the electronic ICU (eICU) Collaborative Research Database. Among 21,925 patients with CS, an unsupervised ML consensus clustering analysis was conducted. The optimal number of clusters was identified by means of the consensus matrix (CM) heat map, cumulative distribution function (CDF), cluster-consensus plots, and the proportion of ambiguously clustered pairs (PAC) analysis. We calculated the standardized mean difference (SMD) of each variable and used the cutoff of ± 0.3 to identify each cluster’s key features. We examined the relationship between the phenotypes and several clinical endpoints utilizing logistic regression (LR) analysis. Results The consensus cluster analysis identified two clusters (Cluster 1: n = 9,848; Cluster 2: n = 12,077). The key features of patients in Cluster 1, compared with Cluster 2, included: lower blood pressure, lower eGFR (estimated glomerular filtration rate), higher BUN (blood urea nitrogen), higher creatinine, lower albumin, higher potassium, lower bicarbonate, lower red blood cell (RBC), higher red blood cell distribution width (RDW), higher SOFA score, higher APS III score, and higher APACHE IV score on admission. The results of LR analysis showed that the Cluster 2 was associated with lower in-hospital mortality (odds ratio [OR]: 0.374; 95% confidence interval [CI]: 0.347–0.402; P < 0.001), ICU mortality (OR: 0.349; 95% CI: 0.318–0.382; P < 0.001), and the incidence of acute kidney injury (AKI) after admission (OR: 0.478; 95% CI: 0.452–0.505; P < 0.001). Conclusions ML consensus clustering analysis synthesized the pattern of clinical and laboratory data to reveal distinct CS phenotypes with different clinical outcomes