Weakly supervised segmentation on neural compressed histopathology with self-equivariant regularization.

In digital pathology, segmentation is a fundamental task for the diagnosis and treatment of diseases. Existing fully supervised methods often require accurate pixel-level annotations that are both time-consuming and laborious to generate. Typical approaches first pre-process histology images into patches to meet memory constraints and later perform stitching for segmentation; at times leading to lower performance given the lack of global context. Since image level labels are cheaper to acquire, weakly supervised learning is a more practical alternative for training segmentation algorithms. In this work, we present a weakly supervised framework for histopathology segmentation using only image-level labels by refining class activation maps (CAM) with self-supervision. First, we compress gigapixel histology images with an unsupervised contrastive learning technique to retain high-level spatial context. Second, a network is trained on the compressed images to jointly predict image-labels and refine the initial CAMs via self-supervised losses. In particular, we achieve refinement via a pixel correlation module (PCM) that leverages self-attention between the initial CAM and the input to encourage fine-grained activations. Also, we introduce a feature masking technique that performs spatial dropout on the compressed input to suppress low confidence predictions. To effectively train our model, we propose a loss function that includes a classification objective with image-labels, self-supervised regularization and entropy minimization between the CAM predictions. Experimental results on two curated datasets show that our approach is comparable to fully-supervised methods and can outperform existing state-of-the-art patch-based methods. https://github.com/PhilipChicco/wsshisto.

Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis.

To minimize the damage from contaminant accidents in rivers, early identification of the contaminant source is crucial. Thus, in this study, a framework combining Machine Learning (ML) and the Transient Storage zone Model (TSM) was developed to predict the spill location and mass of a contaminant source. The TSM model was employed to simulate non-Fickian Breakthrough Curves (BTCs), which entails relevant information of the contaminant source. Then, the ML models were used to identify the BTC features, characterized by 21 variables, to predict the spill location and mass. The proposed framework was applied to the Gam Creek, South Korea, in which two tracer tests were conducted. In this study, six ML methods were applied for the prediction of spill location and mass, while the most relevant BTC features were selected by Recursive Feature Elimination Cross-Validation (RFECV). Model applications to field data showed that the ensemble Decision tree models, Random Forest (RF) and Xgboost (XGB), were the most efficient and feasible in predicting the contaminant source.

Capsaicin ameliorates cisplatin-induced renal injury through induction of heme oxygenase-1.

Cisplatin is one of the most potent chemotherapy agents. However, its use is limited due to its toxicity in normal tissues, including the kidney and ear. In particular, nephrotoxicity induced by cisplatin is closely associated with oxidative stress and inflammation. Heme oxygenase-1 (HO-1), the rate-limiting enzyme in the heme metabolism, has been implicated in a various cellular processes, such as inflammatory injury and anti-oxidant/oxidant homeostasis. Capsaicin is reported to have therapeutic potential in cisplatin-induced renal failures. However, the mechanisms underlying its protective effects on cisplatin-induced nephrotoxicity remain largely unknown. Herein, we demonstrated that administration of capsaicin ameliorates cisplatin-induced renal dysfunction by assessing the levels of serum creatinine and blood urea nitrogen (BUN) as well as tissue histology. In addition, capsaicin treatment attenuates the expression of inflammatory mediators and oxidative stress markers for renal damage. We also found that capsaicin induces HO-1 expression in kidney tissues and HK-2 cells. Notably, the protective effects of capsaicin were completely abrogated by treatment with either the HO inhibitor ZnPP IX or HO-1 knockdown in HK-2 cells. These results suggest that capsaicin has protective effects against cisplatin-induced renal dysfunction through induction of HO-1 as well as inhibition oxidative stress and inflammation.

V-groove SnO2 nanowire sensors: fabrication and Pt-nanoparticle decoration.

Networked SnO(2) nanowire sensors were achieved using the selective growth of SnO(2) nanowires and their tangling ability, particularly on on-chip V-groove structures, in an effort to overcome the disadvantages imposed on the conventional trench-structured SnO(2) nanowire sensors. The sensing performance of the V-groove-structured SnO(2) nanowire sensors was highly dependent on the geometrical dimension of the groove, being superior to those of their conventional trench-structured counterparts. Pt nanoparticles were decorated on the surface of the networked SnO(2) nanowires via γ-ray radiolysis to enhance the sensing performances of the V-groove sensors whose V-groove widths had been optimized. The V-groove-structured Pt-nanoparticle-decorated SnO(2) nanowire sensors exhibited outstanding and reliable sensing capabilities towards toluene and nitrogen dioxide gases, indicating their potential for use as a platform for chemical gas sensors.

Synthesis of NiO nanofibers and their gas sensing properties.

NiO nanofibers were synthesized by an electrospinning method with polyvinyl alcohol and nickel acetate tetrahydrate as precursor materials. Individual nanofibers consisted of nanograins. A gas sensor has been fabricated using these nanofibers. Its sensing properties to NO2 and benzene were investigated. The sensor exhibited good sensitivity and dynamic properties for the tested gases. All these results suggest that the electrospinning-synthesized NiO nanofibers hold promise for realizing sensitive and reliable gas sensors.

Phosphorescent blue organic light-emitting diodes with new bipolar host materials.

We report narrow band gap bipolar host materials, CbPr-3 (9,9'-[(3,3'-Biphenyl-3.3'-yl-bipyridine)-1,3-biphenyl]bis-9H-carbazole) and Bim-4 (9,9'-[5-(1-phenyl-1H-benzimadazol-2yl)-1,3-phenylene] bis-9H-carbazole), for blue phosphorescent OLEDs application. These two bipolar hosts have high triplet energy of > 2.9 eV, capable of reducing the driving voltages and improving efficiencies. Significant low driving voltages of 7.4 and 6.6 V were obtained for CbPr-3 and Bim-4 hosts, compared with 9.0 V of the commonly used host, mCP (1,3-bis(9-carbazolyl)benzene). At a given constant luminance of 1000 cd/m2, the power efficiency of both the bipolar host devices was enhanced by 2.5 times.

Improvement in sensing properties of SnO2 nanowires by functionalizing with Pt nanodots synthesized by gamma-ray radiolysis.

Selectively-grown networked SnO2 nanowires were functionalized with Pt nanodots by the radiolysis process. NO2 sensing characteristics of Pt-functionalized SnO2 nanowires were compared with those of bare SnO2 nanowires. The results demonstrate that the Pt functionalization greatly enhances the sensitivity and response time in SnO2 nanowire-based gas sensors. The enhancement is likely to be associated with the spillover effect and/or easy dissociation of NO2 into more active chemical species by the catalytic effect of Pt.

Significant enhancement of the NO2 sensing capability in networked SnO2 nanowires by Au nanoparticles synthesized via γ-ray radiolysis.

γ-Ray radiolysis was applied to the synthesis of Au nanoparticles. The growth behavior of Au nanoparticles was systematically investigated as a function of the processing parameters under γ-ray radiolysis. The surface of the networked SnO(2) nanowires fabricated through the vapor-phase selective growth process was uniformly functionalized with the Au nanoparticles by the γ-ray radiolysis process. Au nanoparticle-functionalized SnO(2) nanowires were compared to bare SnO(2) nanowires in terms of the NO(2) sensing characteristics. Au functionalization sharply improved the sensitivity and response time of SnO(2) nanowire-based gas sensors, most likely due to the spillover and the catalytic effects of Au nanoparticles. The methodology used in this work can be easily extended to synthesize various combinations of metal nanoparticles and oxide nanowires, which may be useful materials for use in detecting hazardous substances.

Functionalization of selectively grown networked SnO2 nanowires with Pd nanodots by γ-ray radiolysis.

γ-ray radiolysis is applied to synthesizing Pd nanodots on networked SnO(2) nanowires. The growth behavior of Pd nanodots is systematically investigated as a function of the precursor concentration, illumination intensity, and exposure time of the γ-rays. These factors greatly influence the growth behavior of the Pd nanodots. Selectively grown networked SnO(2) nanowires are uniformly functionalized with Pd nanodots by the radiolysis process. The NO(2) sensing characteristics of the Pd-functionalized SnO(2) nanowires are compared with those of bare SnO(2) nanowires. The results indicate that γ-ray radiolysis is an attractive means of functionalizing the surface of oxide nanowires with catalytic Pd nanodots. Moreover, the Pd-functionalization greatly enhances the sensitivity and response time in SnO(2) nanowire-based gas sensors.

Exciplex emission from electroluminescent ladder-type pentaphenylene oligomers bearing both electron- and hole-accepting substituents.

We examine the photophysical properties of ladder-type pentaphenylenes, which have been prepared as prototypical "all-in-one" emissive materials bearing both electron-accepting (diaryloxadiazole) and electron-donating (triphenylamine) units. We find that donor-acceptor interactions are very dependent on the nature of the connectivity of these groups to the main pentaphenylene chain. When the oxadiazole and triphenylamine units were substituted on opposite sides of the pi-conjugated pentaphenylene chromophore, photoluminescence with long lifetimes typical of exciplex-like species was observed, while being significantly quenched by intermolecular charge separation between the substituents. By contrast, when the triphenylamine units were attached at the ends of the chromophore, no such effects were observed and a blue/green photoluminescence was obtained with very high quantum efficiency. In this latter configuration, evidence of ambipolar charge transport and a blue/green electroluminescence were additionally observed.