Developing a weakly supervised deep learning framework for breast cancer diagnosis with HR status based on mammography images.

The status of hormone receptors (HR) at the molecular level is crucial for accurate diagnosis and effective treatment of breast cancer. Meanwhile, mammography is an effective screening method for detecting breast cancer, which significantly improve survival. However, diagnosing the molecular status of breast cancer involves a pathological biopsy, which can affect the accuracy of the diagnosis. To non-invasively diagnose the hormone receptor (HR) status of breast cancer and reduced manual annotation, we proposed a weakly supervised deep learning framework BSNet which detected breast cancer with HR status and benign tumors. BSNet was trained on 2321 multi-view mammography cases from female undergoing digital mammography for the general population at Harbin Medical University Cancer Hospital in Heilongjiang Province during the period 2017-2018 and was validated on the external cohort. The average AUCs of BSNet on the test set and the external validation set were 0.89 and 0.92, respectively. BSNet demonstrated excellent performance in non-invasive breast cancer diagnosis with HR status, using multiple mammography views without pixel annotation. Furthermore, we developed a web server (http://bsnet.edbc.org) for easy use. BSNet described high-dimensional mammography of breast cancer subtypes, which helped inform early management options.

A Spatiotemporal Brain Network Analysis of Alzheimer's Disease Based on Persistent Homology.

Current brain network studies based on persistent homology mainly focus on the spatial evolution over multiple spatial scales, and there is little research on the evolution of a spatiotemporal brain network of Alzheimer's disease (AD). This paper proposed a persistent homology-based method by combining multiple temporal windows and spatial scales to study the spatiotemporal evolution of brain functional networks. Specifically, a time-sliding window method was performed to establish a spatiotemporal network, and the persistent homology-based features of such a network were obtained. We evaluated our proposed method using the resting-state functional MRI (rs-fMRI) data set from Alzheimer's Disease Neuroimaging Initiative (ADNI) with 31 patients with AD and 37 normal controls (NCs). In the statistical analysis experiment, most network properties showed a better statistical power in spatiotemporal networks than in spatial networks. Moreover, compared to the standard graph theory properties in spatiotemporal networks, the persistent homology-based features detected more significant differences between the groups. In the clustering experiment, the brain networks on the sliding windows of all subjects were clustered into two highly structured connection states. Compared to the NC group, the AD group showed a longer residence time and a higher window ratio in a weak connection state, which may be because patients with AD have not established a firm connection. In summary, we constructed a spatiotemporal brain network containing more detailed information, and the dynamic spatiotemporal brain network analysis method based on persistent homology provides stronger adaptability and robustness in revealing the abnormalities of the functional organization of patients with AD.

Directly decorated CeY zeolite for O2-selective adsorption in O2/N2 separation at ambient temperature.

The traditional zeolites used in air separation are generally N2-selective adsorbents. It was found for the first time that the O2/N2 adsorption selectivity can be reversed by directly decorating the Ce metal ion sites of a traditional Y zeolite with imidazole molecules, which results in a novel O2 adsorbent. The O2/N2 selectivity changes from 0.9 to 1.6 under normal conditions, and most importantly, the O2 adsorbent is recyclable. The in situ XPS characterization results indicate that the imidazole modification can change the electronic state of Ce in the Y zeolite and increase its affinity for O2, which is confirmed by theoretical calculations. Dynamic breakthrough adsorption experiments show that the adsorbent has significant application potential in air separation.

Efficient Separation of Xylene Isomers by a Pillar-Layer Metal-Organic Framework.

The separation of xylene isomers is one of the most challenging issues in the chemical industry because of the similarity of their boiling points and kinetic diameters. This study focuses on the use of pillar-layer MOF-Co(aip)(bpy)0.5 for adsorption and separation of xylene isomers. It was found that Co(aip)(bpy)0.5 exhibited a significant para-selectivity in liquid-phase competitive adsorption of xylene isomers, and the competitive separation factors reached as high as 30 for p-xylene versus m-xylene and 16 for p-xylene versus o-xylene. Desorption experiments further confirmed the preferential adsorption of p-xylene on the adsorbent. Molecular simulations and calculations revealed that the order of interaction strengths for xylene molecules and the adsorbent framework was p-xylene ≫ o-xylene ≈ m-xylene, which illustrated the selective adsorption phenomena arising from the mechanism for microscopic interactions. This work broadens the application of pillar-layer MOF materials in the field of xylene isomer adsorption and separation.

Preparation of Efficient Ultraviolet-Protective Transparent Coating by Using a Titanium-Containing Hybrid Oligomer.

Ultraviolet (UV) radiation is closely related to people's lives, but excess UV exposure has led to a series of problems. UV protection technology plays a vital role in our life. The most commonly adopted UV protection technology is to use UV-absorbing materials to make protective coatings, including sunscreen cream for human skin and sunscreen coating for materials. Conventional organic UV-protective coatings have low stability and are sensitive to heat, while inorganic UV-protective coating with highly efficient UV-protective performance usually need high processing temperatures and exhibit low transparency. Here, we report a Ti-PEG-Si cross-linked inorganic-organic hybrid material, which exhibits good UV-absorbing performance. By using these UV-absorbing materials, an efficient transparent UV-absorbing coating could be easily prepared at room temperature (298 K). The UV-absorbing coating is mainly composed of titanium and silicon connected by PEG200. PEG200 as a cross-linker can improve the UV-absorption performance of the coating and increase its visible light transmittance. At the same time, the existence of PEG200 can effectively increase the stability and elasticity of the coating and maintain its mechanical properties after UV irradiation. Furthermore, the coating could maintain highly UV-protective performance and could be transparent even after thermal treatment at high temperature (973 K). From this point of view, the hybrid materials have considerable application potential in next-generation UV protective coatings, especially with their utilization in heat-sensitive substrates or under high-temperature conditions.

Separation of Xylene Isomers in the Anion-Pillared Square Grid Material SIFSIX-1-Cu.

Xylene isomer separation is considered one of the seven separation challenges that changed the world. In addition, the high-energy demand of xylene separation highlights the need for efficient novel adsorbents. Herein, the liquid-phase separation potential of the anion-pillared hybrid material SIFSIX-1-Cu was studied for preferential adsorption of o-xylene and m-xylene over p-xylene, which was inspired by a previous complexation crystallization method for separating m-xylene. We report detailed experimental liquid-phase adsorption experiments, yielding selectivities of 3.0 for o-xylene versus p-xylene and 2.6 for m-xylene versus p-xylene. Our theoretical calculations thus provide a reasonable explanation that the xylene adsorption selectivity is attributed to the C-H⋅⋅⋅F interaction, and the host-guest interaction order agrees with the adsorption priority: o-xylene > m-xylene > p-xylene.

Oxygen-selective adsorption on high-silica LTA zeolite.

The adsorption capacity of O2 and N2 on a LTA-type zeolite can be significantly affected by the change of its Si/Al ratio. With the increase in Si/Al ratio and the decrease in the amount of Na+, the protonated high-silica LTA zeolite changes from being a N2-selective sorbent to an O2-selective sorbent to reverse the O2 and N2 selectivity.

White Matter Brain Network Research in Alzheimer's Disease Using Persistent Features.

Despite the severe social burden caused by Alzheimer's disease (AD), no drug than can change the disease progression has been identified yet. The structural brain network research provides an opportunity to understand physiological deterioration caused by AD and its precursor, mild cognitive impairment (MCI). Recently, persistent homology has been used to study brain network dynamics and characterize the global network organization. However, it is unclear how these parameters reflect changes in structural brain networks of patients with AD or MCI. In this study, our previously proposed persistent features and various traditional graph-theoretical measures are used to quantify the topological property of white matter (WM) network in 150 subjects with diffusion tensor imaging (DTI). We found significant differences in these measures among AD, MCI, and normal controls (NC) under different brain parcellation schemes. The decreased network integration and increased network segregation are presented in AD and MCI. Moreover, the persistent homology-based measures demonstrated stronger statistical capability and robustness than traditional graph-theoretic measures, suggesting that they represent a more sensitive approach to detect altered brain structures and to better understand AD symptomology at the network level. These findings contribute to an increased understanding of structural connectome in AD and provide a novel approach to potentially track the progression of AD.

Enhanced Propene/Propane Separation by Directional Decoration of the 12-Membered Rings of Mordenite with ZIF Fragments.

Propene/propane separation is challenging due to the very small difference in molecular sizes, boiling points and condensabilities between these molecules. Herein, we report a strategy of introducing ZIF fragments into traditional mordenite (MOR) zeolite to decorate the 12-membered ring of MOR. After decoration, the originally ineffective zeolite MOR exhibited high kinetic propene/propane selectivities (139 at 25 °C) and achieved efficient propene/propane separation. The propene/propane separation potentials of the resulting adsorbents were further confirmed by breakthrough experiments with equimolar propene/propane (50/50) mixtures.

Metabolic Brain Network Analysis of FDG-PET in Alzheimer's Disease Using Kernel-Based Persistent Features.

Recent research of persistent homology in algebraic topology has shown that the altered network organization of human brain provides a promising indicator of many neuropsychiatric disorders and neurodegenerative diseases. However, the current slope-based approach may not accurately characterize changes of persistent features over graph filtration because such curves are not strictly linear. Moreover, our previous integrated persistent feature (IPF) works well on an rs-fMRI cohort while it has not yet been studied on metabolic brain networks. To address these issues, we propose a novel univariate network measurement, kernel-based IPF (KBI), based on the prior IPF, to quantify the difference between IPF curves. In our experiments, we apply the KBI index to study fluorodeoxyglucose positron emission tomography (FDG-PET) imaging data from 140 subjects with Alzheimer's disease (AD), 280 subjects with mild cognitive impairment (MCI), and 280 healthy normal controls (NC). The results show the disruption of network integration in the progress of AD. Compared to previous persistent homology-based measures, as well as other standard graph-based measures that characterize small-world organization and modular structure, our proposed network index KBI possesses more significant group difference and better classification performance, suggesting that it may be used as an effective preclinical AD imaging biomarker.

Decorated Traditional Zeolites with Subunits of Metal-Organic Frameworks for CH4 /N2 Separation.

Metal-organic frameworks (MOF) materials are promising materials for gas separation, but their application still faces various challenges. A strategy is now reported for introducing subunits of MOFs into traditional zeolite frameworks to obtain applicable adsorbents with advantages of both zeolites and MOFs. The subunits of ZIFs were introduced into zeolite Y and zeolite ZSM-5 for CH4 /N2 separation. Both the molecular simulation and experimental results validated that the IAST CH4 /N2 selectivity of the resulting samples greatly improved (above 8, at 100 kPa and 25 °C) with the incorporation of ZIF subunits into zeolites structure, and the selectivities were obviously higher than that of zeolites and even better than that of ZIFs. This strategy not only gave rise to an efficient adsorbent for CH4 /N2 separation but also provided ideas for design of other adsorption and separation materials.