Quantitative and intuitive liver tumor treatment with multi-constrained planning and holographic augmented reality

In recent years, thermal ablation has become a widely accepted minimal invasive treatment for liver tumor patients. However, surgical planning and performing are still challenging tasks in two aspects: on one hand, surgical planning relies on fulfilling multiple medical constraints, especially for the ablation based on configurations of multiple electrodes. On the other hand, the precise and efficient performance of the percutaneous tumor punctures under free-breathing conditions is hard to achieve because of the high dependency on surgeons' experience. This thesis presents a novel quantitative and intuitive surgical planning and navigation modality for percutaneous respiratory tumor puncture via holographic visualization, which overlays the pre-operative computer-assisted surgical planning and navigation information precisely onto the intra-operative surgical scenario. In the pre-operation stage, we present the versatile approach for the computer-assisted planning of liver tumor thermal ablation, including the multi-electrode configuration for large tumors based on the patient-specific anatomical data and the insertion trajectory determine based on a series of medical constraints. We also build up the internal-external correlation model which represents the liver and tumor state under free-breathing with respect to the skin markers attached to the patient. In the intra-operative stage, we first propose a virtual-real alignment method to precisely superimpose the virtual information on the surgical scenario. Then, a user-friendly collaborative holographic interface is designed for real-time 3D respiratory tumor puncture navigation, which can assist surgeons in fast and accurately localizing needles towards the target step-by-step. In comparison to the state-of-the-art method and manually sketched thermal planning result, our method can achieve compact ablation regions without relying on assumptions of potential needle path search. This navigation system is validated on the static abdominal phantom, in-vivo beagle dogs, or pigs with artificial lesions. Experimental results demonstrate that the proposed holographic augmented reality navigation modality can effectively reduce the needle adjustment for precise puncture. Our surgical navigation system shows its clinical feasibility to provide the quantitative planning of optimal thermal ablation, which allows completely ablating the tumor region as well as reducing the damage of healthy tissue in comparison to the previous state-of-the-art method. In addition, the proposed augmented virtual reality navigation system can effectively improve the precision and reliability in percutaneous hepatic tumor treatment and has the potential to be used for other surgical planning and navigation tasks

The Study and Development of Transaction Subsystem of Data Center

Along with the rapid growth of the enterprise network services, the data center and its management system has been challenged to fulfill new requirements, which mainly deal with the rivalry between management and maintenance, including the server, though in a deluge of number, fails to reach the standard of business requirement of the busy time; low average utilization rate of the sever result in the increase of both sever quantity and management cost and the pressure imposed by their attendant onerous enterprise management and cost. Therefore, based on the prerequisite of the stable operation of the system, to improve the enterprise value by employing management system with higher efficiency and controlling data center is the wave of the future. This paper synthetically accounts for transaction system of data center’s status quo and problems. It analyzed and studied the transaction system of the deserved functions and systematic components date center, and a design of transaction system of data center of the education metropolitan area network was based on the study above. Key words: IDC; Information System; Day-to-day Transaction; File Managemen

Hyper-Relational Knowledge Graph Neural Network for Next POI

With the advancement of mobile technology, Point of Interest (POI) recommendation systems in Location-based Social Networks (LBSN) have brought numerous benefits to both users and companies. Many existing works employ Knowledge Graph (KG) to alleviate the data sparsity issue in LBSN. These approaches primarily focus on modeling the pair-wise relations in LBSN to enrich the semantics and thereby relieve the data sparsity issue. However, existing approaches seldom consider the hyper-relations in LBSN, such as the mobility relation (a 3-ary relation: user-POI-time). This makes the model hard to exploit the semantics accurately. In addition, prior works overlook the rich structural information inherent in KG, which consists of higher-order relations and can further alleviate the impact of data sparsity.To this end, we propose a Hyper-Relational Knowledge Graph Neural Network (HKGNN) model. In HKGNN, a Hyper-Relational Knowledge Graph (HKG) that models the LBSN data is constructed to maintain and exploit the rich semantics of hyper-relations. Then we proposed a Hypergraph Neural Network to utilize the structural information of HKG in a cohesive way. In addition, a self-attention network is used to leverage sequential information and make personalized recommendations. Furthermore, side information, essential in reducing data sparsity by providing background knowledge of POIs, is not fully utilized in current methods. In light of this, we extended the current dataset with available side information to further lessen the impact of data sparsity. Results of experiments on four real-world LBSN datasets demonstrate the effectiveness of our approach compared to existing state-of-the-art methods

Clinical identification and microbiota analysis of Chlamydia psittaci- and Chlamydia abortus- pneumonia by metagenomic next-generation sequencing

IntroductionRecently, the incidence of chlamydial pneumonia caused by rare pathogens such as C. psittaci or C. abortus has shown a significant upward trend. The non-specific clinical manifestations and the limitations of traditional pathogen identification methods determine that chlamydial pneumonia is likely to be poorly diagnosed or even misdiagnosed, and may further result in delayed treatment or unnecessary antibiotic use. mNGS's non-preference and high sensitivity give us the opportunity to obtain more sensitive detection results than traditional methods for rare pathogens such as C. psittaci or C. abortus. MethodsIn the present study, we investigated both the pathogenic profile characteristics and the lower respiratory tract microbiota of pneumonia patients with different chlamydial infection patterns using mNGS.ResultsMore co-infecting pathogens were found to be detectable in clinical samples from patients infected with C. psittaci compared to C. abortus, suggesting that patients infected with C. psittaci may have a higher risk of mixed infection, which in turn leads to more severe clinical symptoms and a longer disease course cycle. Further, we also used mNGS data to analyze for the first time the characteristic differences in the lower respiratory tract microbiota of patients with and without chlamydial pneumonia, the impact of the pattern of Chlamydia infection on the lower respiratory tract microbiota, and the clinical relevance of these characteristics. Significantly different profiles of lower respiratory tract microbiota and microecological diversity were found among different clinical subgroups, and in particular, mixed infections with C. psittaci and C. abortus resulted in lower lung microbiota diversity, suggesting that chlamydial infections shape the unique lung microbiota pathology, while mixed infections with different Chlamydia may have important effects on the composition and diversity of the lung microbiota. DiscussionThe present study provides possible evidences supporting the close correlation between chlamydial infection, altered microbial diversity in patients' lungs and clinical parameters associated with infection or inflammation in patients, which also provides a new research direction to better understand the pathogenic mechanisms of pulmonary infections caused by Chlamydia

Ferroptosis-associated circular RNAs: Opportunities and challenges in the diagnosis and treatment of cancer

Ferroptosis is an emerging form of non-apoptotic regulated cell death which is different from cell death mechanisms such as autophagy, apoptosis and necrosis. It is characterized by iron-dependent lipid peroxide accumulation. Circular RNA (circRNA) is a newly studied evolutionarily conserved type of non-coding RNA with a covalent closed-loop structure. It exhibits universality, conservatism, stability and particularity. At present, the functions that have been studied and found include microRNA sponge, protein scaffold, transcription regulation, translation and production of peptides, etc. CircRNA can be used as a biomarker of tumors and is a hotspot in RNA biology research. Studies have shown that ferroptosis can participate in tumor regulation through the circRNA molecular pathway and then affect cancer progression, which may become a direction of cancer diagnosis and treatment in the future. This paper reviews the molecular biological mechanism of ferroptosis and the role of circular RNA in tumors and summarizes the circRNA related to ferroptosis in tumors, which may inspire research prospects for the precise prevention and treatment of cancer in the future

ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge Graphs

Question answering over temporal knowledge graphs (TKGQA) has recently found increasing interest. TKGQA requires temporal reasoning techniques to extract the relevant information from temporal knowledge bases. The only existing TKGQA dataset, i.e., CronQuestions, consists of temporal questions based on the facts from a fixed time period, where a temporal knowledge graph (TKG) spanning the same period can be fully used for answer inference, allowing the TKGQA models to use even the future knowledge to answer the questions based on the past facts. In real-world scenarios, however, it is also common that given the knowledge until now, we wish the TKGQA systems to answer the questions asking about the future. As humans constantly seek plans for the future, building TKGQA systems for answering such forecasting questions is important. Nevertheless, this has still been unexplored in previous research. In this paper, we propose a novel task: forecasting question answering over temporal knowledge graphs. We also propose a large-scale TKGQA benchmark dataset, i.e., ForecastTKGQuestions, for this task. It includes three types of questions, i.e., entity prediction, yes-no, and fact reasoning questions. For every forecasting question in our dataset, QA models can only have access to the TKG information before the timestamp annotated in the given question for answer inference. We find that the state-of-the-art TKGQA methods perform poorly on forecasting questions, and they are unable to answer yes-no questions and fact reasoning questions. To this end, we propose ForecastTKGQA, a TKGQA model that employs a TKG forecasting module for future inference, to answer all three types of questions. Experimental results show that ForecastTKGQA outperforms recent TKGQA methods on the entity prediction questions, and it also shows great effectiveness in answering the other two types of questions.Comment: Accepted to ISWC 202

Identification of ZNF26 as a Prognostic Biomarker in Colorectal Cancer by an Integrated Bioinformatic Analysis

The dysregulation of transcriptional factors (TFs) leads to malignant growth and the development of colorectal cancer (CRC). Herein, we sought to identify the transcription factors relevant to the prognosis of colorectal cancer patients. We found 526 differentially expressed TFs using the TCGA database of colorectal cancer patients (n = 544) for the differential analysis of TFs (n = 1,665) with 210 upregulated genes as well as 316 downregulated genes. Subsequently, GO analysis and KEGG pathway analysis were performed for these differential genes for investigating their pathways and function. At the same time, we established a genetic risk scoring model for predicting the overall survival (OS) by using the mRNA expression levels of these differentially regulated TFs, and defined the CRC into low and high-risk categories which showed significant survival differences. The genetic risk scoring model included four high-risk genes (HSF4, HEYL, SIX2, and ZNF26) and two low-risk genes (ETS2 and SALL1), and validated the OS in two GEO databases (p = 0.0023 for the GSE17536, p = 0.0193 for the GSE29623). To analyze the genetic and epigenetic changes of these six risk-related TFs, a unified bioinformatics analysis was conducted. Among them, ZNF26 is progressive in CRC and its high expression is linked with a poor diagnosis as well. Knockdown of ZNF26 inhibits the proliferative capacity of CRC cells. Moreover, the positive association between ZNF26 and cyclins (CDK2, CCNE2, CDK6, CHEK1) was also identified. Therefore, as a novel biomarker, ZNF26 may be a promising candidate in the diagnosis and prognostic evaluation of colorectal cancer

An Improved Modulation Strategy for Single-phase Three-level Neutral-point-clamped Converter in Critical Conduction Mode

Two-level totem-pole power factor correction (PFC) converters in critical conduction mode (CRM) suffer from the wide regulation range of switching frequency. Besides, in high-frequency applications, the number of switching times increases, resulting in significant switching losses. To solve these issues, this paper proposes an improved modulation strategy for the single-phase three-level neutral-point-clamped (NPC) converter in CRM with PFC. By optimizing the discharging strategy and switching state sequence, the switching frequency and its variation range have been efficiently reduced. The detailed performance analysis is also presented regarding the switching frequency, the average switching times, and the effect of voltage gain. A 2 kW prototype is built to verify the effectiveness of the proposed modulation strategy and analysis results. Compared with the totem-pole PFC converter, the switching frequency regulation range of the three-level PFC converter is reduced by 36%, and the average switching times is reduced by 45%. The experimental result also shows a 1.2% higher efficiency for the three-level PFC converter in the full load range

Long-term N addition reduced the diversity of arbuscular mycorrhizal fungi and understory herbs of a Korean pine plantation in northern China

With the development of agriculture and industry, the increase in nitrogen (N) deposition has caused widespread concern among scientists. Although emission reduction policies have slowed N releases in Europe and North America, the threat to biodiversity cannot be ignored. Arbuscular mycorrhizal (AM) fungi play an important role in the establishment and maintenance of plant communities in forest ecosystems, and both their distribution and diversity have vital ecological functions. Therefore, we analyzed the effects of long-term N addition on AM fungi and understory herbaceous plants in a Korean pine plantation in northern China. The soil properties, community structure, and diversity of AM fungi and understory herbaceous plants were detected at different concentrations of NH4NO3 (0, 20, 40, 80 kg N ha−1 year−1) after 7 years. The results showed that long-term N deposition decreased soil pH, increased soil ammonium content, and caused significant fluctuations in P elements. N deposition improved the stability of soil aggregates by increasing the content of glomalin-related soil protein (GRSP) and changed the AM fungal community composition. The Glomus genus was more adaptable to the acidic soil treated with the highest N concentration. The species of AM fungi, understory herbaceous plants, and the biomass of fine roots were decreased under long-term N deposition. The fine root biomass was reduced by 78.6% in the highest N concentration treatment. In summary, we concluded that long-term N deposition could alter soil pH, the distribution of N, P elements, and the soil aggregate fractions, and reduce AM fungal and understory herb diversity. The importance of AM fungi in maintaining forest ecosystem diversity was verified under long-term N deposition