128 research outputs found
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Corrosion of current-collector materials in Li-ion cells
The primary current-collector materials being used in lithium-ion cells are susceptible to environmental degradation: aluminum to pitting corrosion and copper to environmentally assisted cracking. Pitting occurs at the highly oxidizing potentials associated with the positive-electrode charge condition. However, the pitting mechanism is more complex than that typically observed in aqueous systems in that the pits are filled with a mixed metal/oxide product and exist as mounds or nodules on the surface. Electrochemical impedance was shown to be an effective analytical tool for quantification and verification of visual observations and trends. Two fluorocarbon-based coatings were shown to improve the resistance of Al to localized pitting. Finally, environmental cracking of copper can occur at or near the lithium potential and only if specific metallurgical conditions exist (work hardening and large grain size)
How useful is Active Learning for Image-based Plant Phenotyping?
Deep learning models have been successfully deployed for a diverse array of
image-based plant phenotyping applications including disease detection and
classification. However, successful deployment of supervised deep learning
models requires large amount of labeled data, which is a significant challenge
in plant science (and most biological) domains due to the inherent complexity.
Specifically, data annotation is costly, laborious, time consuming and needs
domain expertise for phenotyping tasks, especially for diseases. To overcome
this challenge, active learning algorithms have been proposed that reduce the
amount of labeling needed by deep learning models to achieve good predictive
performance. Active learning methods adaptively select samples to annotate
using an acquisition function to achieve maximum (classification) performance
under a fixed labeling budget. We report the performance of four different
active learning methods, (1) Deep Bayesian Active Learning (DBAL), (2) Entropy,
(3) Least Confidence, and (4) Coreset, with conventional random sampling-based
annotation for two different image-based classification datasets. The first
image dataset consists of soybean [Glycine max L. (Merr.)] leaves belonging to
eight different soybean stresses and a healthy class, and the second consists
of nine different weed species from the field. For a fixed labeling budget, we
observed that the classification performance of deep learning models with
active learning-based acquisition strategies is better than random
sampling-based acquisition for both datasets. The integration of active
learning strategies for data annotation can help mitigate labelling challenges
in the plant sciences applications particularly where deep domain knowledge is
required
Protein alterations associated with temozolomide resistance in subclones of human glioblastoma cell lines
Temozolomide (TMZ) is the standard chemotherapeutic agent for human malignant glioma, but intrinsic or acquired chemoresistance represents a major obstacle to successful treatment of this highly lethal group of tumours. Obtaining better understanding of the molecular mechanisms underlying TMZ resistance in malignant glioma is important for the development of better treatment strategies. We have successfully established a passage control line (D54-C10) and resistant variants (D54-P5 and D54-P10) from the parental TMZ-sensitive malignant glioma cell line D54-C0. The resistant sub-cell lines showed alterations in cell morphology, enhanced cell adhesion, increased migration capacities, and cell cycle arrests. Proteomic analysis identified a set of proteins that showed gradual changes in expression according to their 50% inhibitory concentration (IC50). Successful validation was provided by transcript profiling in another malignant glioma cell line U87-MG and its resistant counterparts. Moreover, three of the identified proteins (vimentin, cathepsin D and prolyl 4-hydroxylase, beta polypeptide) were confirmed to be upregulated in high-grade glioma. Our data suggest that acquired TMZ resistance in human malignant glioma is associated with promotion of malignant phenotypes, and our reported molecular candidates may serve not only as markers of chemoresistance but also as potential therapeutic targets in the treatment of TMZ-resistant human malignant glioma, providing a platform for future investigations
A pilot clinical study of Δ9-tetrahydrocannabinol in patients with recurrent glioblastoma multiforme
Δ9-Tetrahydrocannabinol (THC) and other cannabinoids inhibit tumour growth and angiogenesis in animal models, so their potential application as antitumoral drugs has been suggested. However, the antitumoral effect of cannabinoids has never been tested in humans. Here we report the first clinical study aimed at assessing cannabinoid antitumoral action, specifically a pilot phase I trial in which nine patients with recurrent glioblastoma multiforme were administered THC intratumoraly. The patients had previously failed standard therapy (surgery and radiotherapy) and had clear evidence of tumour progression. The primary end point of the study was to determine the safety of intracranial THC administration. We also evaluated THC action on the length of survival and various tumour-cell parameters. A dose escalation regimen for THC administration was assessed. Cannabinoid delivery was safe and could be achieved without overt psychoactive effects. Median survival of the cohort from the beginning of cannabinoid administration was 24 weeks (95% confidence interval: 15–33). Δ9-Tetrahydrocannabinol inhibited tumour-cell proliferation in vitro and decreased tumour-cell Ki67 immunostaining when administered to two patients. The fair safety profile of THC, together with its possible antiproliferative action on tumour cells reported here and in other studies, may set the basis for future trials aimed at evaluating the potential antitumoral activity of cannabinoids
12‐Crown‐4 Ether‐Assisted Enhancement of Ionic Conductivity and Interfacial Kinetics in Polyethylene Oxide Electrolytes
Erratum: “Cycloaliphatic Epoxide-Based Photocured Gelled Electrolytes for Secondary Lithium Battery Applications. Electrochemical Kinetic Studies” [J. Electrochem. Soc., 141, 1414 (1994)]
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