A simplified Bcl-2 network model reveals quantitative determinants of cell-to-cell variation in sensitivity to anti-mitotic chemotherapeutics

Anti-mitotic drugs constitute a major class of cytotoxic chemotherapeutics used in the clinic, killing cancer cells by inducing prolonged mitotic arrest that activates intrinsic apoptosis. Anti-mitotics-induced apoptosis is known to involve degradation of anti-apoptotic Bcl-2 proteins during mitotic arrest; however, it remains unclear how this mechanism accounts for significant heterogeneity observed in the cell death responses both within and between cancer cell types. To unravel quantitative determinants underlying variability in anti-mitotic drug response, we constructed a single-cell dynamical Bcl-2 network model describing cell death control during mitotic arrest, and constrained the model using experimental data from four representative cancer cell lines. The modeling analysis revealed that, given a variable, slowly accumulating pro-apoptotic signal arising from anti-apoptotic protein degradation, generation of a switch-like apoptotic response requires formation of pro-apoptotic Bak complexes with hundreds of subunits, suggesting a crucial role for high-order cooperativity. Moreover, we found that cell-type variation in susceptibility to drug-induced mitotic death arises primarily from differential expression of the anti-apoptotic proteins Bcl-xL and Mcl-1 relative to Bak. The dependence of anti-mitotic drug response on Bcl-xL and Mcl-1 that we derived from the modeling analysis provides a quantitative measure to predict sensitivity of distinct cancer cells to anti-mitotic drug treatment

Avocado Fruit Pulp Transcriptomes in the after-Ripening Process

Avocado is an important tropical fruit whose after-ripening process is still poorly understood. The fatty acid, phenolics, flavonoids, and tannins were analyzed in ‘Lisa’ avocado (Persea americana Mill. ‘Lisa’) fruit pulp during after-ripening. The transcriptome was analyzed to screen for transcripts associated with the aforementioned after-ripening parameters. The results showed that there were no significant differences in the total fatty acid content among the preclimacteric, climacteric, and postclimacteric stages. Nevertheless, the concentrations of C18:3 (α-linolenic acid) were significantly higher in the climacteric and postclimacteric stages than the preclimacteric stage. RNAseq generated 235,082 transcripts and 151,545 unigenes. In addition, 4,324 DEGs were produced among the three stages. KEGG analysis of the DEGs suggested the pathways about “α-linolenic acid metabolism, unsaturated fatty acid biosynthesis”, “fatty acid degradation”, “linoleic acid metabolism and fatty acid biosynthesis”, “linoleic acid metabolism and fatty acid elongation”, and “fatty acid elongation” may all contribute to the C18:3 variations in ‘Lisa’ avocado fruit pulp. Several transcription factors, including the ethylene-related transcription factors, such as NAC, MYB, bHLH, and WRKY, were also identified in the DEGs database. This study generated transcript data and screened the transcription factors involved in the avocado after-ripening process. This information could be used to control after-ripening in avocado and maintain fruit quality during storage

Clinicopathology and Recurrence Analysis of 44 Jaw Aneurysmal Bone Cyst Cases: A Literature Review

In the past half-century, considerable attention has been paid to oral and maxillofacial skeletal cyst, however, aneurysmal bone cyst (ABC), unlike other common bone diseases, still contours numerous unanswered questions in terms of classification, etiology and pathological mechanism. The purpose of this article was to evaluate the proportion of primary ABC and secondary ABC, and to assess the recurrence of ABC and related factors. A methodical search of Embase, MEDLINE, Cochrane Library, Web of Science was conducted for well-documented jaw aneurysmal bone cyst (JABC) cases. One hundred thirty-one articles were identified after database searching and 31 of them were included in our study for further research with 44 JABC cases. All the articles were analyzed by two separate authors. About 25% of the reported jaw aneurysmal bone cyst was secondary. Both the pathological classification and surgical treatment had a significant influence on recurrence rate (P = 0.0082, P = 0.0022), while patients' age or radiographic features rarely affected prognosis. Jaw aneurysmal bone cysts can present variable clinical and histological presentations. Recurrence may be attributed to omittance of underlying potential blood supply or conservative surgical protocol

Quantum delayed-choice experiment with a beam splitter in a quantum superposition

A quantum system can behave as a wave or as a particle, depending on the experimental arrangement. When for example measuring a photon using a Mach-Zehnder interferometer, the photon acts as a wave if the second beam-splitter is inserted, but as a particle if this beam-splitter is omitted. The decision of whether or not to insert this beam-splitter can be made after the photon has entered the interferometer, as in Wheeler's famous delayed-choice thought experiment. In recent quantum versions of this experiment, this decision is controlled by a quantum ancilla, while the beam splitter is itself still a classical object. Here we propose and realize a variant of the quantum delayed-choice experiment. We configure a superconducting quantum circuit as a Ramsey interferometer, where the element that acts as the first beam-splitter can be put in a quantum superposition of its active and inactive states, as verified by the negative values of its Wigner function. We show that this enables the wave and particle aspects of the system to be observed with a single setup, without involving an ancilla that is not itself a part of the interferometer. We also study the transition of this quantum beam-splitter from a quantum to a classical object due to decoherence, as observed by monitoring the interferometer output.Comment: 9 pages, 7 figures, Accepted by Physical Review Letter

A simplified Bcl-2 network model reveals quantitative determinants of cell-to-cell variation in sensitivity to anti-mitotic chemotherapeutics

Anti-mitotic drugs constitute a major class of cytotoxic chemotherapeutics used in the clinic, killing cancer cells by inducing prolonged mitotic arrest that activates intrinsic apoptosis. Anti-mitotics-induced apoptosis is known to involve degradation of anti-apoptotic Bcl-2 proteins during mitotic arrest; however, it remains unclear how this mechanism accounts for significant heterogeneity observed in the cell death responses both within and between cancer cell types. To unravel quantitative determinants underlying variability in anti-mitotic drug response, we constructed a single-cell dynamical Bcl-2 network model describing cell death control during mitotic arrest, and constrained the model using experimental data from four representative cancer cell lines. The modeling analysis revealed that, given a variable, slowly accumulating pro-apoptotic signal arising from anti-apoptotic protein degradation, generation of a switch-like apoptotic response requires formation of pro-apoptotic Bak complexes with hundreds of subunits, suggesting a crucial role for high-order cooperativity. Moreover, we found that cell-type variation in susceptibility to drug-induced mitotic death arises primarily from differential expression of the anti-apoptotic proteins Bcl-xL and Mcl-1 relative to Bak. The dependence of anti-mitotic drug response on Bcl-xL and Mcl-1 that we derived from the modeling analysis provides a quantitative measure to predict sensitivity of distinct cancer cells to anti-mitotic drug treatment

Detecting and removing visual distractors for video aesthetic enhancement

Personal videos often contain visual distractors, which are objects that are accidentally captured that can distract viewers from focusing on the main subjects. We propose a method to automatically detect and localize these distractors through learning from a manually labeled dataset. To achieve spatially and temporally coherent detection, we propose extracting features at the Temporal-Superpixel (TSP) level using a traditional SVM-based learning framework. We also experiment with end-to-end learning using Convolutional Neural Networks (CNNs), which achieves slightly higher performance than other methods. The classification result is further refined in a post-processing step based on graph-cut optimization. Experimental results show that our method achieves an accuracy of 81% and a recall of 86%. We demonstrate several ways of removing the detected distractors to improve the video quality, including video hole filling; video frame replacement; and camera path re-planning. The user study results show that our method can significantly improve the aesthetic quality of videos

Arts therapies for mental disorders in COVID-19 patients: a comprehensive review

Background and objectiveThe COVID-19 global pandemic has necessitated the urgency for innovative mental health interventions. We performed a comprehensive review of the available literature on the utility and efficacy of arts therapies in treating mental health problems, with special emphasis on their deployment during the COVID-19 pandemic, aiming to provide some evidence for the application of this therapy.MethodsThe potential studies were systematically sourced from five authoritative databases: PubMed, Embase, the Cochrane Library, Web of Science, and the CNKI database. The evaluation of these studies was conducted based on stringent criteria, including validity, suitability, therapeutic potential, and consistency. Each piece of included literature was meticulously scored in accordance with these criteria, thus ensuring the inclusion of only the most robust studies in this review. The data from these Randomized Controlled Trials (RCTs) were carefully extracted using the PICO(S) framework, ensuring a comprehensive and systemic approach to data collection. In order to emphasize the variability in the effects of differing arts therapies on COVID-19-induced psychiatric disturbances, the sourced literature was systematically categorized and scrutinized based on distinct modalities.ResultsOut of the 7,250 sourced articles, 16 satisfied the inclusion conditions. The therapies were predominantly meditation (n = 7), supplemented by individual studies on color therapy (n = 3), music therapy (n = 2), and single studies on horticultural therapy, dance therapy, mindfulness and music therapy, and yoga and music therapy (n = 4 collectively). These various forms of arts therapies had a positive short to medium-term impact on the mental health of COVID-19 patients. Besides improving patients' physical and mental health, these therapies can also be employed to mitigate mental health issues among healthcare professionals.ConclusionThe COVID-19 pandemic has profound and long-lasting implications for public mental health. Diverse forms of arts therapies are potentially effective in addressing related psychiatric symptoms. The integration of artificial intelligence might further enhance the efficacy and scalability of arts therapies in future implementations