100 research outputs found
A Novel Iron Phosphate Cement Derived from Copper Smelting Slag and its Early Age Hydration Mechanism
Copper slag (CS), a by-product of copper smelting, is normally stockpiled, leading to wastes of resource and space as well as environment pollution. It has not been massively reutilized as a supplementary cementitious material in Portland cement due to its low reactivity. In the present study, CS is for the first time utilized as the base component to prepare an iron phosphate cement (IPC) by reacting with ammonium dihydrogen phosphate (ADP) at room temperature. The influence of the raw materials mass ratio (CS/ADP) on the microstructure and performance of IPC pastes are investigated. It is found that the compressive strength of IPC pastes at all ages is not a monotonic function of CS/ADP, and the paste with CS/ADP of 2.0 gives the highest strengths, i.e., 26.8, 38.9 and 47.5 MPa at 1, 3 and 28 d, respectively. The crystalline phases including FeH2P3O10·H2O and FePO4 are formed as the main reaction products to bind the unreacted CS particles. The early age hydration of IPC is found to be a multi-stage process, involving the initial dissolution of ADP and iron-containing phases of CS, the formation of FeH2P3O10·H2O, the initial generation of FePO4, and the attainment of the hydration reaction equilibrium. Unlike the magnesium phosphate cement, a redox reaction of Fe(â
Ą) into Fe(â
ą) occurs due to the suitable range of pH and oxidation-reduction potential of the IPC system during the hydration reaction
A Comparative Study of Theoretical Graph Models for Characterizing Structural Networks of Human Brain
Previous studies have investigated both structural and functional brain networks via graph-theoretical methods. However, there is an important issue that has not been adequately discussed before: what is the optimal theoretical graph model for describing the structural networks of human brain? In this paper, we perform a comparative study to address this problem. Firstly, large-scale cortical regions of interest (ROIs) are localized by recently developed and validated brain reference system named Dense Individualized Common Connectivity-based Cortical Landmarks (DICCCOL) to address the limitations in the identification of the brain network ROIs in previous studies. Then, we construct structural brain networks based on diffusion tensor imaging (DTI) data. Afterwards, the global and local graph properties of the constructed structural brain networks are measured using the state-of-the-art graph analysis algorithms and tools and are further compared with seven popular theoretical graph models. In addition, we compare the topological properties between two graph models, namely, stickiness-index-based model (STICKY) and scale-free gene duplication model (SF-GD), that have higher similarity with the real structural brain networks in terms of global and local graph properties. Our experimental results suggest that among the seven theoretical graph models compared in this study, STICKY and SF-GD models have better performances in characterizing the structural human brain network
Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video
Real-time eyeblink detection in the wild can widely serve for fatigue
detection, face anti-spoofing, emotion analysis, etc. The existing research
efforts generally focus on single-person cases towards trimmed video. However,
multi-person scenario within untrimmed videos is also important for practical
applications, which has not been well concerned yet. To address this, we shed
light on this research field for the first time with essential contributions on
dataset, theory, and practices. In particular, a large-scale dataset termed
MPEblink that involves 686 untrimmed videos with 8748 eyeblink events is
proposed under multi-person conditions. The samples are captured from
unconstrained films to reveal "in the wild" characteristics. Meanwhile, a
real-time multi-person eyeblink detection method is also proposed. Being
different from the existing counterparts, our proposition runs in a one-stage
spatio-temporal way with end-to-end learning capacity. Specifically, it
simultaneously addresses the sub-tasks of face detection, face tracking, and
human instance-level eyeblink detection. This paradigm holds 2 main advantages:
(1) eyeblink features can be facilitated via the face's global context (e.g.,
head pose and illumination condition) with joint optimization and interaction,
and (2) addressing these sub-tasks in parallel instead of sequential manner can
save time remarkably to meet the real-time running requirement. Experiments on
MPEblink verify the essential challenges of real-time multi-person eyeblink
detection in the wild for untrimmed video. Our method also outperforms existing
approaches by large margins and with a high inference speed.Comment: Accepted by CVPR 202
Germline Mutations in Patients With Early-Onset Prostate Cancer.
Objective: To investigate the inherited mutations and their association with clinical features and treatment response in young-onset prostate cancer patients.
Method: Targeted gene sequencing on 139 tumor susceptibility genes was conducted with a total of 24 patients diagnosed with PCa under the age of 63 years old. Meanwhile, the related clinical information of those patients is collected and analyzed.
Results: Sixty-two germline mutations in 45 genes were verified in 22 patients.
Conclusion: Mutations in DRGs are more prevalent in early-onset PCa with advanced clinical stages, and these patients had shorter progression-free survival. ADT Combined with either radiotherapy or chemotherapy may be effective in treating PCa caused by HRR-related gene mutations
ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data
Radiology report generation, as a key step in medical image analysis, is
critical to the quantitative analysis of clinically informed decision-making
levels. However, complex and diverse radiology reports with cross-source
heterogeneity pose a huge generalizability challenge to the current methods
under massive data volume, mainly because the style and normativity of
radiology reports are obviously distinctive among institutions, body regions
inspected and radiologists. Recently, the advent of large language models (LLM)
offers great potential for recognizing signs of health conditions. To resolve
the above problem, we collaborate with the Second Xiangya Hospital in China and
propose ChatRadio-Valuer based on the LLM, a tailored model for automatic
radiology report generation that learns generalizable representations and
provides a basis pattern for model adaptation in sophisticated analysts' cases.
Specifically, ChatRadio-Valuer is trained based on the radiology reports from a
single institution by means of supervised fine-tuning, and then adapted to
disease diagnosis tasks for human multi-system evaluation (i.e., chest,
abdomen, muscle-skeleton, head, and maxillofacial neck) from six different
institutions in clinical-level events. The clinical dataset utilized in this
study encompasses a remarkable total of \textbf{332,673} observations. From the
comprehensive results on engineering indicators, clinical efficacy and
deployment cost metrics, it can be shown that ChatRadio-Valuer consistently
outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and
GPT-4 et al., in terms of the diseases diagnosis from radiology reports.
ChatRadio-Valuer provides an effective avenue to boost model generalization
performance and alleviate the annotation workload of experts to enable the
promotion of clinical AI applications in radiology reports
Expanded encyclopaedias of DNA elements in the human and mouse genomes
All data are available on the ENCODE data portal: www.encodeproject. org. All code is available on GitHub from the links provided in the methods section. Code related to the Registry of cCREs can be found at https:// github.com/weng-lab/ENCODE-cCREs. Code related to SCREEN can be found at https://github.com/weng-lab/SCREEN.© The Author(s) 2020. The human and mouse genomes contain instructions that specify RNAs and proteins and govern the timing, magnitude, and cellular context of their production. To better delineate these elements, phase III of the Encyclopedia of DNA Elements (ENCODE) Project has expanded analysis of the cell and tissue repertoires of RNA transcription, chromatin structure and modification, DNA methylation, chromatin looping, and occupancy by transcription factors and RNA-binding proteins. Here we summarize these efforts, which have produced 5,992 new experimental datasets, including systematic determinations across mouse fetal development. All data are available through the ENCODE data portal (https://www.encodeproject.org), including phase II ENCODE1 and Roadmap Epigenomics2 data. We have developed a registry of 926,535 human and 339,815 mouse candidate cis-regulatory elements, covering 7.9 and 3.4% of their respective genomes, by integrating selected datatypes associated with gene regulation, and constructed a web-based server (SCREEN; http://screen.encodeproject.org) to provide flexible, user-defined access to this resource. Collectively, the ENCODE data and registry provide an expansive resource for the scientific community to build a better understanding of the organization and function of the human and mouse genomes.This work was supported by grants from the NIH under U01HG007019, U01HG007033, U01HG007036, U01HG007037, U41HG006992, U41HG006993, U41HG006994, U41HG006995, U41HG006996, U41HG006997, U41HG006998, U41HG006999, U41HG007000, U41HG007001, U41HG007002, U41HG007003, U54HG006991, U54HG006997, U54HG006998, U54HG007004, U54HG007005, U54HG007010 and UM1HG009442
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