Search

The segmentation of high-grade gliomas (HGG) using magnetic resonance imaging (MRI) data is clinically meaningful in neurosurgical practice, but a challenging task. Currently, most segmentation methods are supervised learning with labeled training sets. Although these methods work well in most cases, they typically require time-consuming manual labeling and pre-trained models. In this work, we propose an automatically unsupervised segmentation toolbox based on the clustering algorithm and morphological processing, named AUCseg. With our toolbox, the whole tumor was first extracted by clustering on T2-FLAIR images. Then, based on the mask acquired with whole tumor segmentation, the enhancing tumor was segmented on the post-contrast T1-weighted images (T1-CE) using clustering methods. Finally, the necrotic regions were segmented by morphological processing or clustering on T2-weighted images. Compared with K-means, Mini-batch K-means, and Fuzzy C Means (FCM), the Gaussian Mixture Model (GMM) clustering performs the best in our toolbox. We did a multi-sided evaluation of our toolbox in the BraTS2018 dataset and demonstrated that the whole tumor, tumor core, and enhancing tumor can be automatically segmented using default hyper-parameters with Dice score 0.8209, 0.7087, and 0.7254, respectively. The computing time of our toolbox for each case is around 22 seconds, which is at least 3 times faster than other state-of-the-art unsupervised methods. In addition, our toolbox has an option to perform semi-automatic segmentation via manually setup hyper-parameters, which could improve the segmentation performance. Our toolbox, AUCseg, is publicly available on Github. (https://github.com/Haifengtao/AUCseg).

Simple Summary Metastasis remains the greatest cause of fatalities in breast cancer patients world-wide. The process of metastases is highly complex, and the current research efforts in this area are still rather fragmented. The revolution of genomic profiling methods to analyze samples from human and animal models dramatically improved our understanding of breast cancer metastasis. This article summarizes the recent breakthroughs in genomic analyses of breast cancer metastasis and discusses their implications for prognostic and therapeutic applications. Abstract Breast cancer (BC) is one of the most diagnosed cancers worldwide and is the second cause of cancer related death in women. The most frequent cause of BC-related deaths, like many cancers, is metastasis. However, metastasis is a complicated and poorly understood process for which there is a shortage of accurate prognostic indicators and effective treatments. With the rapid and ever-evolving development and application of genomic sequencing technologies, many novel molecules were identified that play previously unappreciated and important roles in the various stages of metastasis. In this review, we summarize current advancements in the functional genomic analysis of BC metastasis and discuss about the potential prognostic and therapeutic implications from the recent genomic findings.

BACKGROUND It is challenging to distinguish intestinal tuberculosis from Crohn’s disease due to dynamic changes in epidemiology and similar clinical characteristics. Recent studies have shown that polymorphisms in genes involved in the interleukin (IL)-23/IL-17 axis may affect intestinal mucosal immunity by affecting the differentiation of Th17 cells. AIM To investigate the specific single-nucleotide polymorphisms (SNPs) in genes involved in the IL-23/IL-17 axis and possible pathways that affect susceptibility to intestinal tuberculosis and Crohn's disease. METHODS We analysed 133 patients with intestinal tuberculosis, 128 with Crohn’s disease, and 500 normal controls. DNA was extracted from paraffin-embedded specimens or whole blood. Four SNPs in the IL23/Th17 axis (IL22 rs2227473, IL1β rs1143627, TGFβ rs4803455, and IL17 rs8193036) were genotyped with TaqMan assays. The transcriptional activity levels of different genotypes of rs2227473 were detected by dual luciferase reporter gene assay. The expression of IL-22R1 in different intestinal diseases was detected by immunohistochemistry. RESULTS The A allele frequency of rs2227473 (P = 0.030, odds ratio = 0.60, 95% confidence interval: 0.37-0.95) showed an abnormal distribution between intestinal tuberculosis and healthy controls. The presence of the A allele was associated with a higher IL-22 transcriptional activity (P < 0.05). In addition, IL-22R1 was expressed in intestinal lymphoid tissues, especially under conditions of intestinal tuberculosis, and highly expressed in macrophage-derived Langhans giant cells. The results of immunohistochemistry showed that the expression of IL-22R1 in patients with Crohn's disease and intestinal tuberculosis was significantly higher than that in patients with intestinal polyps and colon cancer (P < 0.01). CONCLUSION High IL-22 expression seems to be a protective factor for intestinal tuberculosis. IL-22R1 is expressed in Langhans giant cells, suggesting that the IL-22/IL-22R1 system links adaptive and innate immunity.

While anti-reflective properties of pyramid texture are widely used, their use for thermal radiation control has received relatively little attention and the understanding of geometric parameters for design optimization is not well established. Here we use finite-difference time-domain simulations in conjunction with an algorithm to optimize thermal characteristics of micropyramid-textured metallic, ceramic, and polymer materials. Our simulations indicate that the pyramid height-to-base ratio is an effective parameter in developing an engineered thermal response. For nickel, the micropyramids with 2���4 height-to-base ratios over 0.5���4 ��m base spans provide near-perfect absorption in 300���2500 nm wavelengths. The electric field analysis shows the optical properties are driven by the effects of localized resonance and field confinement. Our thermal cost function-based optimization has led to micropyramid texture that can have a significant impact on heating or cooling such as the solar absorption increase in nickel from 337 to 982 W/m2, the thermal emission increase in alumina from 106 to 170 W/m2, and the thermal emission increase in PDMS from 160 to 172 W/m2. This work not only provides the understanding of micropyramid properties for thermal radiation control but also presents an algorithmic process that could be used for efficient optical-thermal optimization of geometries beyond micropyramids.

Automatic segmentation of mouse brain structures in magnetic resonance (MR) images plays a crucial role in understanding brain organization and function in both basic and translational research. Due to fundamental differences in contrast, image size, and anatomical structure between the human and mouse brains, existing neuroimaging analysis tools designed for the human brain are not readily applicable to the mouse brain. To address this problem, we propose a generative adversarial network (GAN)-based network, named MouseGAN, to synthesize multiple MRI modalities and to segment mouse brain structures using a single MRI modality. MouseGAN contains a modality translation module to project multi-modality image features into a shared latent content space that encodes modality-invariant brain structures and a modality-specific attribute. In addition, the content encoder learned from the modality translation module is reused for the segmentation module to improve the structural segmentation. Our results demonstrate that MouseGAN can segment up to 50 mouse brain structures with an averaged dice coefficient of 83%, which is a 7–10% increase compared to baseline U-Net segmentation. To the best of our knowledge, it is the first Atlas-free tool for segmenting mouse brain structures from MRI data. Another benefit is that with the help of the shared encoder, MouseGAN can handle missing MRI modalities without significant sacrifice of the performance. We will release our code and trained model to promote its free usage for neuroimaging applications.

Currently, reliable, robust and ready-to-use CT-based tools for prediction of COVID-19 progression are still lacking. To address this problem, we present DABC-Net, a novel deep learning (DL) tool that combines a 2D U-net for intra-slice spatial information processing, and a recurrent LSTM network to leverage inter-slice context, for automatic volumetric segmentation of lung and pneumonia lesions. We evaluate DABC-Net on more than 10,000 radiologists-labeled CT slices from four different cohorts. Compared to state-of-the-art segmentation tools, DABC-Net is much faster, more robust, and able to estimate segmentation uncertainty. Based only on the first two CT scans within 3 days after admission from 656 longitudinal CT scans, the AUC of our DBAC-Net for disease progression prediction reaches 93%. We release our tool as a GUI for patient-specific prediction of pneumonia progression, to provide clinicians with additional assistance to triage patients at early days after the diagnosis and to optimize the assignment of limited medical resources, which is of particular importance in current critical COVID-19 pandemic.

Purpose: Accurate segmentation of lung and infection in COVID-19 CT scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that are impractical to obtain from a single institution, especially when radiologists are busy fighting the coronavirus disease. Furthermore, it is hard to compare current COVID-19 CT segmentation methods as they are developed on different datasets, trained in different settings, and evaluated with different metrics. Methods: To promote the development of data-efficient deep learning methods, in this paper, we built three benchmarks for lung and infection segmentation based on 70 annotated COVID-19 cases, which contain current active research areas, e.g., few-shot learning, domain generalization, and knowledge transfer. For a fair comparison among different segmentation methods, we also provide standard training, validation and testing splits, evaluation metrics and, the corresponding code. Results: Based on the state-of-the-art network, we provide more than 40 pre-trained baseline models, which not only serve as out-of-the-box segmentation tools but also save computational time for researchers who are interested in COVID-19 lung and infection segmentation. We achieve average Dice Similarity Coefficient (DSC) scores of 97.3\%, 97.7\%, and 67.3\% and average Normalized Surface Dice (NSD) scores of 90.6\%, 91.4\%, and 70.0\% for left lung, right lung, and infection, respectively. Conclusions: To the best of our knowledge, this work presents the first data-efficient learning benchmark for medical image segmentation and the largest number of pre-trained models up to now. All these resources are publicly available, and our work lays the foundation for promoting the development of deep learning methods for efficient COVID-19 CT segmentation with limited data., accepted for publication in Medical Physics

Immunosuppressive tumor microenvironment (TME) and ascites-derived spheroids in ovarian cancer (OC) facilitate tumor growth and progression, and also pose major obstacles for cancer therapy. The molecular pathways involved in the OC-TME interactions, how the crosstalk impinges on OC aggression and chemoresistance are not well-characterized. Here, we demonstrate that tumor-derived UBR5, an E3 ligase overexpressed in human OC associated with poor prognosis, is essential for OC progression principally by promoting tumor-associated macrophage recruitment and activation via key chemokines and cytokines. UBR5 is also required to sustain cell-intrinsic β-catenin-mediated signaling to promote cellular adhesion/colonization and organoid formation by controlling the p53 protein level. OC-specific targeting of UBR5 strongly augments the survival benefit of conventional chemotherapy and immunotherapies. This work provides mechanistic insights into the novel oncogene-like functions of UBR5 in regulating the OC-TME crosstalk and suggests that UBR5 is a potential therapeutic target in OC treatment for modulating the TME and cancer stemness., Ovarian cancer cells often metastasize to the peritoneal cavity, forming spheroid-like structures and promoting a highly immunosuppressive tumor microenvironment. Here, the authors show that the ubiquitin ligase UBR5 is required for ovarian cancer growth and metastasis, sustaining spheroid formation and the infiltration of immunosuppressive tumor associated macrophages.

Phase-changing materials are promising due to their sharp temperature dependent characteristics and have high potential of being integrated in optical switching and sensing techniques. Among such materials, vanadium dioxide (VO2) is the most utilitarian because of its transition temperature being close to the room-temperature. VO2-based bolometers utilize the material’s large temperature coefficient of resistivity to detect infrared radiation. However, to achieve large sensitivity, the active radiation absorption area needs to be large enough that allows sufficient temperature buildup from incident radiation absorbed by VO2, thus requiring large pixel dimen- sion and degrading the spatial resolution of bolometric sensing. Moreover, the absorption by the VO2 material is not optimized for a specific frequency band in most of the applications. On the other hand, plasmonic nanos- tructures can be tuned and designed to selectively and efficiently absorb a specific band of the incident radiation for local heating and thermal imaging. In this work, we propose to incorporate plasmonic nanostructures with VO2 nanowires that amplifies the slope of impedance change due to the thermal variations to achieve a higher sensitivity. We present the numerical analysis of the mid-infrared electromagnetic radiation absorption by the proposed detector showing near-perfect absorption by the plasmonic absorbers. Besides, the thermal buildup and the nanowire resistance change is predicted for different substrate, as the substrate plays a big role in heat distribution. We show high sensitivity and ultra-low noise equivalent temperature difference (NEDT) by our novel bolometric detector. We also discuss the fabrication of the VO2.

Urbanization-induced land-use change will lead to variations in the demand and supply of ecosystem services, thus significantly affecting regional ecosystem services. The continuous degradation of ecosystem functions has become a serious problem for humanity to solve. Therefore, quantitative analysis of the corresponding impact of land-use change on ecosystem service value (ESV) is important to socio-economic development and ecological protection. The Anhui province in China has experienced rapid urbanization in recent years, and ecological environmental remediation and protection have become important goals for regional development. In this paper, the province of Anhui has been selected as a case of study, we analyzed the land-use change using Landsat images from 2000, 2005, 2010, and 2015. We then adjusted the equivalent factor of ESV per unit area and estimated the ESV of Anhui province from 2000 to 2015 to analyze the impact of land-use change on ESV. Our results show that (1) paddy field is the main land-use type in Anhui province, the built-up land area has continuously increased, and the water area has continuously decreased, (2) the total ESV of Anhui province decreased from 30,015.58 &times, 107 CNY in 2000 to 29,683.74 &times, 107 CNY in 2015 (the rate of change was &minus, 1.11%), and regulating services make the greatest contribution to ESV, and (3) land-use change has led to severe ESV variations, especially for the expansion of water area and built-up land. Our study results provide useful insights for the development of land-use management and environmental protection policies in Anhui province.

While hollow microspheres and various porous structures have received much attention for solar reflection in the recent literature, their fundamental determinants of optical properties and material selection criteria are relatively little known. Here, we study hollow microspheres with varying refractive index and extinction coefficient and identify their role in determining the solar reflectivity. Our simulations based on finite-difference time-domain method show the effects of refractive index between 1.5 and 100 and extinction coefficient between 10−6–100 in the wavelength region of 0.2–2.4 μm and explain how the reflectivity of hollow microspheres is attributed to a combination of strong backscattering and limited absorption. Our analysis indicates that ceramic materials with a high refractive index and a low extinction coefficient such as Y2O3 are promising. When Y2O3 hollow microspheres are randomly distributed with the diameter ranging from 0.5 to 1 μm, our simulation shows the solar reflectivity reaches 0.97 even at 300 μm thickness, and a diffusion theory-based model predicts the solar reflectivity to exceed 0.98 at 500 μm or 0.99 at 1 mm thickness. Our findings can guide optimal designs of hollow microspheres and related porous structures toward complete solar reflection and enable breakthroughs in thermal management and deep-space applications.

Conventional surface materials for cooling systems, often based on metals, generally offer excellent mechanical and thermal conduction properties. However, metals are known to be very reflective, and their emissivity is very low, which limits effective radiative heat transfer from the surface to the environment - a factor that is particularly important when there is a high difference in temperature between the surface and ambient. Here we will demonstrate a novel material coating based on silicon that will offer transformative surface cooling and omniphobic properties by controlling the microscale surface texture on silicon wafers. Surface texturing silicon or micro-structuring silicon using maskless chemical assisted etching or reactive-ion etching has received much attention recently for its applications in photonics and solar cells 1–3. Colloquially referred to as “black silicon”, textured silicon surfaces comprised of micro-structured pillars or roughness have been known to absorb (or emit) light very effectively in the visible spectrum, which makes the surface turn black4-6. Past studies have focused on the modified optical properties in the visible to near-infrared spectrum (400nm to 1.3um), relevant mostly to photonic applications, but we show that the optical properties maintain a high emissivity across the near-mid IR spectrum of 9–13 uM. The high emissivity across the mid-IR creates a unique opportunity to produce an inexpensive, highly adaptable, and scalable layer which demonstrates enhanced passive surface cooling relative to conventional materials. We will demonstrate a novel maskless process that utilizes a NaOH wet etch which generates micro-pyramidal structures on the order of 5–20 um in height that accomplishes these objectives. Furthermore, by texturing the surface geometry, textured silicon can also be manufactured to offer either hydrophobic or hydrophilic properties, opening the possibility for a material that can have optimized conductive, radiative, and convective properties.

While dynamic photonic materials have attracted much attention and there are well-known examples of color-changing species in nature, dynamic thermal control via modulation of optical properties has made relatively little progress. By replicating unique properties of desert ants and chameleons, here we present a stretchable selective emitter based on corrugated nickel that can modulate the emissivity to provide dynamic thermal control on human bodies. By evaporating nickel on a pre-strained polymer, we create 700-nm periodic corrugations that increase the nickel absorptivity from 0.3 to 0.7 in 0.2–2.5 µm wavelengths due to multiple scattering, as supported by spectroscopy and computations. The optical change is reversible and accompanies ambient surface temperature variations in 305–315 K. We demonstrate a wearable system, and the corrugated nickel on a human body at 309 K allows a heat flux of 62 Wm−2 out of the skin when stretched and 79 Wm−2 into the skin when released.

Background. Because of the similarity of intestinal tuberculosis and Crohn’s disease in disease phenotype, differential diagnosis has always been a clinical problem. Arachidonic acid metabolites play an important role in the inflammatory response of intestinal tuberculosis and Crohn’s disease. Recent studies have shown that the polymorphism locus in the promoter region of LTA4H gene affects LTB4 expression level and the susceptibility to extrapulmonary tuberculosis. Thus, we identified a total of 148 patients with intestinal tuberculosis, 145 with Crohn’s disease, and 700 normal controls in this study. Methods. All the study participants were local Han people from Jiangxi Province in the past eleven years. DNA was extracted from the paraffin-embedded specimens or the whole blood. The LTA4H promoter SNP (rs17525495) was genotyped with TaqMan assay. Results. The T-alleles frequency was not significantly increased in patients with intestinal tuberculosis compared with healthy control group (p=0.630; OR=1.07; 95%CI=0.81-1.41), while patients with Crohn’s disease have significantly increased T allele frequency compared with healthy population (p=0.032; OR=1.34; 95%CI=1.03-1.75). During treatment, the presence of the T allele significantly increased the proportion of Crohn’s patients requiring glucocorticoids (p Conclusions. The T allele of LTA4H gene SNP (rs17525495) is a risk factor for Crohn’s disease instead of intestinal tuberculosis. More importantly, there may be a potential association of the different genotypes of rs17525495 with the treatment efficacy of 5-ASA and glucocorticoids in patients with Crohn’s disease. The association between LTA4H polymorphism and drugs therapeutic effects might contribute to the practice of precision medicine and the prediction of clinical outcomes.

Acute lymphoblastic leukaemia (ALL) is the most prevalent childhood malignancy. Although most children with ALL are cured, there is still a group of patients for which therapy fails owing to severe toxicities and drug resistance. Ginsenoside Rh2 (GRh2), a major bioactive component isolated from Panax ginseng, has been shown to have a therapeutic effect on some tumors. However, the molecular mechanisms of cell death induced by 20(S)-GRh2 in ALL cells remains unclear. In this study, we showed that 20(S)-GRh2 inhibited the cell growth and induced mitochondria-dependent apoptosis and autophagy. But it has no cytotoxic effect on human normal blood cells. Furthermore, autophagy plays a protective role in 20(S)-GRh2-induced apoptosis in ALL cell lines and human primary ALL cells. We demonstrated that either genetic or pharmacologic inhibition of autophagy could be more effective in reducing viability and enhancing 20(S)-GRh2-induced toxicity than 20(S)-GRh2 treatment alone. In addition, inhibition of autophagy could aggravate mitochondrial ROS generation and mitochondrial damage, and then accelerate mitochondria-dependent apoptosis. Taken together, these results suggest that inhibition of autophagy can sensitize ALL cells towards 20(S)-GRh2. The appropriate inhibition of autophagy could provide a powerful strategy to increase the potency of 20(S)-GRh2 as a novel anticancer agent for ALL therapy.

In this Letter, we show that the heat transferred from a thermal composite hot body emitter may be selectively directed in the far field toward a cold body receiver of choice through the enhanced design of both the anisotropic thermal conductivity of the emitter body and its surface emissivity. Specifically, focused radiosity of a representative cylindrical emitter in a preferential direction is attained by optimizing the layout of high and low thermally conductive materials within the solid in combination with an angularly varying emissivity surface profile. The relationship between the multi-body view factor scene and the thermal metamaterial design is clarified by way of numerical experiments. Subsequent gradient-based co-optimization of the thermal composite confirms the working principles of the device and reveals non-intuitive material and surface coating layouts that further enhance the directional radiative intensity of the emitter. The principles are extendable to the engineering of arbitrarily shaped advanced composites for thermal protection systems, energy conservation, or spacecraft low-energy deep-space maneuvering by way of radiation/conduction heat flow control.

In recent years, the PM2.5 (particulate matter with a mean aerodynamic diameter of 2.5 micrometers or less) pollution has become a very serious problem in China. Currently, there are three types of monitoring approaches: government-led monitoring, Wireless Sensor Networks (WSN) approaches and Participatory Urban Sensing (PUS). There are three limitations in these state-of-the-art research approaches: a) the coarse-grained limitation of government-led monitoring, b) the deployment and maintenance cost of WSN approaches, c) the "Black Hole" problem and the "Black Time Window" problem of PMTI-based approaches in PUS. How to overcome these three main limitations is the biggest challenge. To address these limitations, we need a new way to collect PM2.5 data. Nowadays, IoT (Internet of Things) smart devices sold to various customers could steadily and directly collect the PM2.5 data in vast urban areas, but how to obtain wide-spread real PM2.5 data from tens of thousands of smart devices is another challenge. While no existing work has addressed these two challenges, how to address them is an open problem. In this paper, we propose PMViewer, a novel PUS approach to address these two challenges. PMViewer's data are collected from tens of thousands of smart devices called AirBox through a crowdsourcing approach. We aim to offer a fine-grained spatial-temporal resolution for the public to monitor the urban PM2.5 pollution near their locations. PMViewer scrawls data from AirBox's vendor server and parses the data to generate a map view to display real-time urban PM2.5 measurements. In this study, we design, implement and evaluate PMViewer. Evaluation results show that PMViewer efficiently and economically addressed these two challenges described above.

The trends toward higher power, higher frequency, and smaller scale electronics are making heat dissipation ever more challenging. Passive thermal management based on high thermal conductivity materials or through-silicon vias (TSVs) may not provide sufficient cooling for hot spots reaching 1 kW cm-2, and active thermal management by thermoelectric cooling (TEC) may require large power consumption or suffer from a large off-state thermal resistance of thermoelectric materials. Here we address these issues by integrating a holey silicon-based TEC with a TSV that directly draws heat from a hot spot to combine active and passive cooling approaches. Our simulations of the TSV-integrated TEC demonstrate exceptional cooling performance, which reduces the hot spot temperature from 154 °C to 68 °C while dissipating a heat flux of 1 k W cm-2 and consuming 0.5 W for TEC operation. The off-state hot spot temperature, 154 °C, is 24 °C lower than that of the same TEC with no TSV, and the on-state hot spot temperature, 68 °C, is 67 °C lower than that of the same TEC with no TSV. We also investigate the cooling prospects of metal-filled holey silicon by modeling the electron-phonon coupling and size dependent transport phenomena, which can further increase the thermal conductivity anisotropy and improve the TEC performance depending on the metal-to-silicon interfacial resistance. These results show the combined passive and active cooling in TSV-integrated TEC offers effective hot spot thermal management solutions for advanced electronics.