Traditional Chinese medicine (TCM) has been practiced for thousands of years and at the present time is widely accepted as an alternative treatment for cancer. In this review, we sought to summarize the molecular and cellular mechanisms underlying the chemopreventive and therapeutic activity of TCM, especially that of the Chinese herbal medicine‐derived phytochemicals curcumin, resveratrol, and berberine. Numerous genes have been reported to be involved when using TCM treatments and so we have selectively highlighted the role of a number of oncogene and tumor suppressor genes in TCM therapy. In addition, the impact of TCM treatment on DNA methylation, histone modification, and the regulation of noncoding RNAs is discussed. Furthermore, we have highlighted studies of TCM therapy that modulate the tumor microenvironment and eliminate cancer stem cells. The information compiled in this review will serve as a solid foundation to formulate hypotheses for future studies on TCM‐based cancer therapy.

Traditional passwords are inadequate as cryptographic keys, as they are easy to forge and are vulnerable to guessing. Human biometrics have been proposed as a promising alternative due to their intrinsic nature. Electrocardiogram (ECG) is an emerging biometric that is extremely difficult to forge and circumvent, but has not yet been heavily investigated for cryptographic key generation. ECG has challenges with respect to immunity to noise, abnormalities, etc. In this paper, we propose a novel key generation approach that extracts keys from real-valued ECG features with high reliability and entropy in mind. Our technique, called interval optimized mapping bit allocation (IOMBA), is applied to normal and abnormal ECG signals under multiple session conditions. We also investigate IOMBA in the context of different feature extraction methods, such as wavelet, discrete cosine transform, etc., to find the best method for feature extraction. Experiments of IOMBA show that 217-, 38-, and 100-bit keys with 99.9%, 97.4%, and 95% average reliability and high entropy can be extracted from normal, abnormal, and multiple session ECG signals, respectively. By allowing more errors or lowering entropy, key lengths can be further increased by tunable parameters of IOMBA, which can be useful in other applications. While IOMBA is demonstrated on ECG, it should be useful for other biometrics as well.

A physical unclonable function (PUF) is a structure that produces a unique response, with an issued challenge (input), which can be used as an identifier or a cryptographic key. SRAM PUFs create unique responses upon power up as certain SRAM cells output a "1" or "0" with high probability due to uncontrollable process variations. A current challenge in SRAM PUFs is their sensitivity to temperature and voltage variations as well as aging. It is always challenging to make SRAM PUFs reliable and unique with algorithms that isolate stable and uncorrelated bits quickly with minimal testing (enrollment). In this paper, we explore the selection of stable and uncorrelated bits through enrollment under different conditions (temperature and voltage) and also by exploiting previously undiscovered interactions between neighboring SRAM cells. We propose University of Connecticut, Storrs, CT, USA neighbor influenced cell selection algorithm (NICSA) with the help of metrics that analyze the impact of each neighboring cell and each enrollment condition. The proposed NICSA helps to identify the "best" cells and conditions for stable bit selection. Besides reliability, SRAM PUF can be less unique due to systematic correlation among chips. We study the systematic correlation between SRAMs power-up values to find the uncorrelated cells among chips for better uniqueness. We have analyzed data from 5 ISSI, 3 IDT, and 3 Cypress SRAMs and our metrics identify the best neighborhood size (16 stable neighbors) and best enrollment condition pair high temperature, high voltage, and low temperature for NICSA.

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Predicting changes in plant diversity in response to human activities represents one of the major challenges facing ecologists and land managers striving for sustainable ecosystem management. Classical field studies have emphasized the importance of community primary productivity in regulating changes in plant species richness. However, experimental studies have yielded inconsistent empirical evidence, suggesting that primary productivity is not the sole determinant of plant diversity. Recent work has shown that more accurate predictions of changes in species diversity can be achieved by combining measures of species’ cover and height into an index of space resource utilization (SRU). While the SRU approach provides reliable predictions, it is time‐consuming and requires extensive taxonomic expertise. Ecosystem processes and plant community structure are likely driven primarily by dominant species (mass ratio effect). Within communities, it is likely that dominant and rare species have opposite contributions to overall biodiversity trends. We, therefore, suggest that better species richness predictions can be achieved by utilizing SRU assessments of only the dominant species (SRUD), as compared to SRU or biomass of the entire community. Here, we assess the ability of these measures to predict changes in plant diversity as driven by nutrient addition and herbivore exclusion. First, we tested our hypotheses by carrying out a detailed analysis in an alpine grassland that measured all species within the community. Next, we assessed the broader applicability of our approach by measuring the first three dominant species for five additional experimental grassland sites across a wide geographic and habitat range. We show that SRUD outperforms community biomass, as well as community SRU, in predicting biodiversity dynamics in response to nutrients and herbivores in an alpine grassland. Across our additional sites, SRUD yielded far better predictions of changes in species richness than community biomass, demonstrating the robustness and generalizable nature of this approach. Synthesis. The SRUD approach provides a simple, non‐destructive and more accurate means to monitor and predict the impact of global change drivers and management interventions on plant communities, thereby facilitating efforts to maintain and recover plant diversity.