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Tissue-clearing and labeling techniques have revolutionized brain-wide imaging and analysis, yet their application to clinical formalin-fixed paraffin-embedded (FFPE) blocks remains challenging. We introduce HIF-Clear, a novel method for efficiently clearing and labeling centimeter-thick FFPE specimens using elevated temperature and concentrated detergents. HIF-Clear with multi-round immunolabeling reveals neuron circuitry regulating multiple neurotransmitter systems in a whole FFPE mouse brain and is able to be used as the evaluation of disease treatment efficiency. HIF-Clear also supports expansion microscopy and can be performed on a non-sectioned 15-year-old FFPE specimen, as well as a 3-month formalin-fixed mouse brain. Thus, HIF-Clear represents a feasible approach for researching archived FFPE specimens for future neuroscientific and 3D neuropathological analyses.

Agriculture contributes nearly a quarter of global greenhouse gas (GHG) emissions, which is motivating interest in adopting certain farming practices that have the potential to reduce GHG emissions or sequester carbon in soil. The related GHG emission (including N2O and CH4) and changes in soil carbon stock are defined here as “agricultural carbon outcomes”. Accurate quantification of agricultural carbon outcomes is the basis for achieving emission reductions for agriculture, but existing approaches for measuring carbon outcomes (including direct measurements, emission factors, and process-based modeling) fall short of achieving the required accuracy and scalability necessary to support credible, verifiable, and cost-effective measurement and improvement of these carbon outcomes. Here we propose a foundational and scalable framework to quantify field-level carbon outcomes for farmland, which is based on the holistic carbon balance of the agroecosystem: Agroecosystem Carbon Outcomes = Environment (E) × Management (M) × Crop (C). Following a comprehensive review of the scientific challenges associated with existing approaches, as well as their tradeoffs between cost and accuracy, we propose that the most viable path for the quantification of field-level carbon outcomes in agricultural land is through an effective integration of various approaches (e.g. diverse observations, sensor/in-situ data, and modeling), defined as the “System-of-Systems” solution. Such a “System-of-Systems” solution should simultaneously comprise the following components: (1) scalable collection of ground truth data and cross-scale sensing of environment variables (E), management practices (M), and crop conditions (C) at the local field level; (2) advanced modeling with necessary processes to support the quantification of carbon outcomes; (3) systematic Model-Data Fusion (MDF), i.e. robust and efficient methods to integrate sensing data and models at each local farmland level; (4) high computation efficiency and artificial intelligence (AI) to scale to millions of individual fields with low cost; and (5) robust and multi-tier validation systems and infrastructures to ensure solution fidelity and true scalability, i.e. the ability of a solution to perform robustly with accepted accuracy on all targeted fields. In this regard, we provide here the detailed scientific rationale, current progress, and future research and development (R&D) priorities to achieve different components of the “System-of-Systems” solution, thus accomplishing the Environment×Management×Crop framework to quantify field-level agricultural carbon outcomes.

OBJECTIVE: Novel therapeutic strategies are emerging with the increased understanding of the underlying mechanisms of human osteosarcoma. This current study tends to decipher the potentially critical role of DEP domain-containing 1 (DEPDC1), a tumor-related gene, during the progression of osteosarcoma. METHODS: Bioinformatics analysis of 25,035 genes from the National Center for Biotechnology Information (NCBI) databases was performed to screen differentially expressed genes between osteosarcoma and normal control groups, complemented by the examination of 85 clinical osteosarcoma specimens. Furthermore, the manipulation of DEPDC1 expression levels by using silencing RNA (siRNA) or lentiviral vector intervention on human osteosarcoma cells was performed to reveal its role and interactions in in vitro and in vivo settings. RESULTS: Gene expression profile analysis and immunohistochemical (IHC) examination suggested that DEPDC1 is highly expressed in human osteosarcoma cells and tumor tissue. The silencing of DEPDC1 arrested osteosarcoma cell proliferation, promoted apoptosis, and ceased tumor metastasis. Studies involving clinical human osteosarcoma cases exhibited a strong correlation of DEPDC1 over-expressed osteosarcoma specimens with a reduced patient survival rate. CONCLUSIONS: Collectively, this study demonstrated that DEPDC1 is a critical driver in the promotion of osteosarcoma progression and results in poor patient prognosis. Genetically targeting or pharmacologically inhibiting DEPDC1 may serve as a promising strategy for treating human osteosarcoma.

Abstract Age and sex have effect on atherosclerosis. This study aimed to investigate their effect on non-stenotic intracranial atherosclerotic plaque (NIAP) in embolic stroke of undetermined source (ESUS) using high-resolution magnetic resonance imaging (HR-MRI). We retrospectively recruited consecutive ESUS patients who underwent intracranial HR-MRI to assess the plaque characteristics (remodeling index [RI], plaque burden [PB], fibrous cap [FC], discontinuity of plaque surface [DPS], intraplaque hemorrhage [IPH] and complicated plaque [CP]). We divided patients into three groups ( 0.05). In age group of 60–74 years, men were associated with higher PB (66.27 ± 9.17% vs 60.91 ± 8.86%, p = 0.017) and RI (1.174 vs 1.156, p = 0.019), higher prevalence of DPS (82.50% vs 60.00%, p = 0.036) and complicated plaque (85.00% vs 63.33%, p = 0.036). For subjects ≥ 75 years old, PB were significantly higher in twomen vs men (68.85 ± 6.14% vs 62.62 ± 7.36%, p = 0.040). In addition, the probability for PBupper (≥ median PB), RIupper (≥ median RI) and vulnerable plaque increased as age increased, and its predictive power for index ESUS was higher in men than women. This study identified age-dependent sex differences in NIAP characteristics of ESUS patients, which will help us clarify their etiology.

Abstract To evaluate the association of intracranial non-stenotic atherosclerotic plaque with cerebral small vessel disease (CSVD) imaging markers in a CSVD population using 3.0 T high-resolution magnetic resonance imaging (HRMRI), which was validated in embolic stroke of undetermined source (ESUS) cohort. We retrospectively recruited consecutive patients who were diagnosed with CSVD or ESUS from January 2015 to December 2019. All patients underwent intracranial HRMRI to assess intracranial non-stenotic atherosclerotic plaques. Baseline and imaging data were collected and were measured among all patients. Among 153 patients with CSVD, there were 59 with intracranial atherosclerotic plaque (IAP) and 94 with non-IAP, including 36 with intracranial atherosclerotic complicated plaque (IACP). Among 227 ESUS patients, there were 155 with IAP and 72 with non-IAP, including 127 with IACP. In the CSVD population, we found that: (1) CSVD burden was associated with IAP (p = 0.036) and IACP (p = 0.008); (2) IAP was associated with white matter hyperintensity (51% vs. 34%; P = 0.039), and IACP was associated with lacunes (69% vs. 35%; P = 0.009) and enlarge perivascular space (69% vs. 39%; P = 0.022). A similar association of CSVD imaging markers with IAP or IACP was found in the ESUS population. Furthermore, the association of unilateral IAP or IACP with CSVD imaging markers of ipsilateral hemisphere was identified in the two cohorts. This is the first report that intracranial non-stenotic atherosclerotic plaque, especially complicated plaque, is closely associated with CSVD imaging markers, which provide further evidence for the association of large artery atherosclerosis with CSVD.