Analysis of G-quadruplexes as environmental sensors: Novel statistical models and computational algorithms enable interpretation of complex gene expression patterns for maize under salt stress conditions

The occurrence of G-quadruplex (G4) structures in both genic and non-genic sequences have been well-documented. However, even in genic regions the biological functions of these motifs remains poorly understood, though their potential to act in a regulatory fashion has been hypothesized. With the recent development of next-generation sequencing technology, we have accumulated genomic and transcriptomic sequences from various species and tissues. Coupled with pattern recognition software that can identify putative G4 sequences, the time is right for tackling the question of whether and how G4’s are involved in regulating gene expression. Previous studies suggested that G4 conformation can be dependent on cation type and concentration, along with G4 motif patterns differences (e.g., number of consecutive guanines). It also has been shown that G4 function may be associated with the location relative to a given gene’s structural elements (transcription start site [TSS], exon/intron boundaries, etc.). My project focused on the expression of G4-containing genes from maize tissues under various abiotic stress conditions, including salt stress, which would be likely to change physiological cation concentrations. I quantified, compared, and visualized expression of G4-containing gene groups by developing and applying novel computational algorithms and statistical models. These methods were packaged into a software program I released on a web server called C-REx (http://c-rex.dill-picl.org/). I found that under salt stress conditions, transcription factors (TFs) with a G4 on the anti-sense strand upstream of the TSS are 455% more likely to be up-regulated than non-G4 genes. Likewise, transcription factors with a G4 on the anti-sense strand just downstream of the TSS are 259% more likely to be up-regulated. In addition, among G4 transcription factors that are up-regulated, heat shock factors are significantly enriched. On the other hand, under salt stress conditions non-TF genes with a G4 on anti-sense strand upstream of the TSS are 157% more likely to be down-regulated, and those with the G4 on the anti-sense strand downstream of the TSS are 124% more likely to be down-regulated. Through G4 sequence feature analysis, we found that the length of G-runs was significantly associated with whether genes were switched ‘on’ or ‘off’ in salt stress conditions. The shortest G-runs were associated with G4 motifs in TF genes that were switched ‘on’ and longest G-runs were associated with G4s in non-TF genes that were switched ‘off’. These findings suggest that salt stress resilience could potentially be improved in maize by selecting for natural gene variants with specific G4 constitutions or by introducing specific G4 motifs of varying lengths into TF and non-TF genes involved in response to salt stress

Single-peak and narrow-band mid-infrared thermal emitters driven by mirror-coupled plasmonic quasi-BIC metasurfaces

Wavelength-selective thermal emitters (WS-EMs) hold considerable appeal due to the scarcity of cost-effective, narrow-band sources in the mid-to-long-wave infrared spectrum. WS-EMs achieved via dielectric materials typically exhibit thermal emission peaks with high quality factors (Q factors), but their optical responses are prone to temperature fluctuations. Metallic EMs, on the other hand, show negligible drifts with temperature changes, but their Q factors usually hover around 10. In this study, we introduce and experimentally verify a novel EM grounded in plasmonic quasi-bound states in the continuum (BICs) within a mirror-coupled system. Our design numerically delivers an ultra-narrowband single peak with a Q factor of approximately 64, and near-unity absorptance that can be freely tuned within an expansive band of more than 10 {\mu}m. By introducing air slots symmetrically, the Q factor can be further augmented to around 100. Multipolar analysis and phase diagrams are presented to elucidate the operational principle. Importantly, our infrared spectral measurements affirm the remarkable resilience of our designs' resonance frequency in the face of temperature fluctuations over 300 degrees Celsius. Additionally, we develop an effective impedance model based on the optical nanoantenna theory to understand how further tuning of the emission properties is achieved through precise engineering of the slot. This research thus heralds the potential of applying plasmonic quasi-BICs in designing ultra-narrowband, temperature-stable thermal emitters in mid-infrared. Moreover, such a concept may be adaptable to other frequency ranges, such as near-infrared, Terahertz, and Gigahertz.Comment: 39 pages, 12 figure

Fundamentals and emerging optical applications of hexagonal boron nitride: a tutorial

Hexagonal boron nitride (hBN), also known as white graphite, is a transparent layered crystal with a wide bandgap. Its crystal structure resembles graphite, featuring layers composed of honeycomb lattices held together through van der Waals forces. The layered crystal structure of hBN facilitates exfoliation into thinner flakes and makes it highly anisotropic in in-plane and out-of-plane directions. Unlike graphite, hBN is both insulating and transparent, making it an ideal material for isolating devices from the environment and acting as a waveguide. As a result, hBN has found extensive applications in optical devices, electronic devices, and quantum photonic devices. This comprehensive tutorial aims to provide readers with a thorough understanding of hBN, covering its synthesis, lattice and spectroscopic characterization, and various applications in optoelectronic and quantum photonic devices. This tutorial is designed for both readers without prior experience in hBN and those with expertise in specific fields seeking to understand its relevance and connections to others

Image polaritons in boron nitride for extreme polariton confinement with low losses

Polaritons in two-dimensional materials provide extreme light confinement that is difficult to achieve with metal plasmonics. However, such tight confinement inevitably increases optical losses through various damping channels. Here we demonstrate that hyperbolic phonon polaritons in hexagonal boron nitride can overcome this fundamental trade-off. Among two observed polariton modes, featuring a symmetric and antisymmetric charge distribution, the latter exhibits lower optical losses and tighter polariton confinement. Far-field excitation and detection of this high-momenta mode becomes possible with our resonator design that can boost the coupling efficiency via virtual polariton modes with image charges that we dub image polaritons. Using these image polaritons, we experimentally observe a record-high effective index of up to 132 and quality factors as high as 501. Further, our phenomenological theory suggests an important role of hyperbolic surface scattering in the damping process of hyperbolic phonon polaritons

Ultrafast evanescent heat transfer across solid interfaces via hyperbolic phonon polaritons in hexagonal boron nitride

The efficiency of phonon-mediated heat transport is limited by the intrinsic atomistic properties of materials, seemingly providing an upper limit to heat transfer in materials and across their interfaces. The typical speeds of conductive transport, which are inherently limited by the chemical bonds and atomic masses, dictate how quickly heat will move in solids. Given that phonon-polaritons, or coupled phonon-photon modes, can propagate at speeds approaching 1 percent of the speed of light - orders of magnitude faster than transport within a pure diffusive phonon conductor - we demonstrate that volume-confined, hyperbolic phonon-polariton(HPhP) modes supported by many biaxial polar crystals can couple energy across solid-solid interfaces at an order of magnitude higher rates than phonon-phonon conduction alone. Using pump-probe thermoreflectance with a mid-infrared, tunable, probe pulse with sub-picosecond resolution, we demonstrate remote and spectrally selective excitation of the HPhP modes in hexagonal boron nitride in response to radiative heating from a thermally emitting gold source. Our work demonstrates a new avenue for interfacial heat transfer based on broadband radiative coupling from a hot spot in a gold film to hBN HPhPs, independent of the broad spectral mismatch between the pump(visible) and probe(mid-IR) pulses employed. This methodology can be used to bypass the intrinsically limiting phonon-phonon conductive pathway, thus providing an alternative means of heat transfer across interfaces. Further, our time-resolved measurements of the temperature changes of the HPhP modes in hBN show that through polaritonic coupling, a material can transfer heat across and away from an interface at rates orders of magnitude faster than diffusive phonon speeds intrinsic to the material, thus demonstrating a pronounced thermal transport enhancement in hBN via phonon-polariton coupling

Response to Persistent ER Stress in Plants: a Multiphasic Process that Transitions Cells from Prosurvival Activities to Cell Death

The unfolded protein response (UPR) is a highly conserved response that protects plants from adverse environmental conditions. The UPR is elicited by endoplasmic reticulum (ER) stress, in which unfolded and misfolded proteins accumulate within the ER. Here, we induced the UPR in maize (Zea mays) seedlings to characterize the molecular events that occur over time during persistent ER stress. We found that a multiphasic program of gene expression was interwoven among other cellular events, including the induction of autophagy. One of the earliest phases involved the degradation by regulated IRE1-dependent RNA degradation (RIDD) of RNA transcripts derived from a family of peroxidase genes. RIDD resulted from the activation of the promiscuous ribonuclease activity of ZmIRE1 that attacks the mRNAs of secreted proteins. This was followed by an upsurge in expression of the canonical UPR genes indirectly driven by ZmIRE1 due to its splicing of Zmbzip60 mRNA to make an active transcription factor that directly upregulates many of the UPR genes. At the peak of UPR gene expression, a global wave of RNA processing led to the production of many aberrant UPR gene transcripts, likely tempering the ER stress response. During later stages of ER stress, ZmIRE1\u27s activity declined as did the expression of survival modulating genes, Bax inhibitor1 and Bcl-2-associated athanogene7, amidst a rising tide of cell death. Thus, in response to persistent ER stress, maize seedlings embark on a course of gene expression and cellular events progressing from adaptive responses to cell death

Personalized chemotherapy selection for patients with triple-negative breast cancer using deep learning

BackgroundPotential uncertainties and overtreatment exist in adjuvant chemotherapy for triple-negative breast cancer (TNBC) patients.ObjectivesThis study aims to explore the performance of deep learning (DL) models in personalized chemotherapy selection and quantify the impact of baseline characteristics on treatment efficacy.MethodsPatients who received treatment recommended by models were compared to those who did not. Overall survival for treatment according to model recommendations was the primary outcome. To mitigate bias, inverse probability treatment weighting (IPTW) was employed. A mixed-effect multivariate linear regression was employed to visualize the influence of certain baseline features of patients on chemotherapy selection.ResultsA total of 10,070 female TNBC patients met the inclusion criteria. Treatment according to Self-Normalizing Balanced (SNB) individual treatment effect for survival data model recommendations was associated with a survival benefit (IPTW-adjusted hazard ratio: 0.53, 95% CI, 0.32–8.60; IPTW-adjusted risk difference: 12.90, 95% CI, 6.99–19.01; IPTW-adjusted the difference in restricted mean survival time: 5.54, 95% CI, 1.36–8.61), which surpassed other models and the National Comprehensive Cancer Network guidelines. No survival benefit for chemotherapy was seen for patients not recommended to receive this treatment. SNB predicted older patients with larger tumors and more positive lymph nodes are the optimal candidates for chemotherapy.ConclusionThese findings suggest that the SNB model may identify patients with TNBC who could benefit from chemotherapy. This novel analytical approach may provide debiased individual survival information and treatment recommendations. Further research is required to validate these models in clinical settings with more features and outcome measurements

Research progress on deep learning in magnetic resonance imaging–based diagnosis and treatment of prostate cancer: a review on the current status and perspectives

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future