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Human papillomaviruses (HPVs) are small double-stranded DNA viruses that infect epithelial cells and cause cervical, anogenital, and oropharyngeal cancers. HPV genome replication relies on the viral E1 and E2 proteins to initiate DNA replication. The first step is the assembly of the E1-E2 complex at the origin of replication. We have examined the role of full-length HPV E1 helicase and its interaction with E2 in pre-initiation complex formation. Electrophoretic mobility shift assays (EMSAs) with purified E1 and E2 proteins revealed that the HPV genome does not have a specific E1 binding site, or such a sequence is not required for pre-initiation complex formation. E1 alone did not show any binding to the origin DNA sequences, while E2 facilitated E1 recruitment to the origin, forming the E1-E2-DNA ternary complex. Formation of such a complex required at least two E2 binding sites. These findings led us to propose a novel mechanism in which E2 dimers serve as the primary recruiters of E1 to form the pre-initiation complex. This study provides new insights into the mechanistic role of E2 in the recruitment of E1 at the origin of HPV DNA replication, enhancing our understanding of HPV biology and potentially informing future therapeutic strategies.

The syndromic autism spectrum disorder (ASD) Timothy syndrome (TS) is caused by a point mutation in the alternatively spliced exon 8A of the calcium channel Cav1.2. Using mouse brain and human induced pluripotent stem cells (iPSCs), we provide evidence that the TS mutation prevents a normal developmental switch in Cav1.2 exon utilization, resulting in persistent expression of gain-of-function mutant channels during neuronal differentiation. In iPSC models, the TS mutation reduces the abundance of SATB2-expressing cortical projection neurons, leading to excess CTIP2+ neurons. We show that expression of TS-Cav1.2 channels in the embryonic mouse cortex recapitulates these differentiation defects in a calcium-dependent manner and that in utero Cav1.2 gain-and-loss of function reciprocally regulates the abundance of these neuronal populations. Our findings support the idea that disruption of developmentally regulated calcium channel splicing patterns instructively alters differentiation in the developing cortex, providing important in vivo insights into the pathophysiology of a syndromic ASD.

During the 1990s, a new paradigm for power sector reform was put forward emphasizing the restructuring of utilities, the creation of regulators, the participation of the private sector, and the establishment of competitive power markets.

Fusarium head blight (FHB) remains one of the most destructive diseases of wheat (Triticum aestivum L.), causing considerable losses in yield and end‐use quality. Phenotyping of FHB resistance traits, Fusarium‐damaged kernels (FDK), and deoxynivalenol (DON), is either prone to human biases or resource expensive, hindering the progress in breeding for FHB‐resistant cultivars. Though genomic selection (GS) can be an effective way to select these traits, inaccurate phenotyping remains a hurdle in exploiting this approach. Here, we used an artificial intelligence (AI)‐based precise FDK estimation that exhibits high heritability and correlation with DON. Further, GS using AI‐based FDK (FDK_QVIS/FDK_QNIR) showed a two‐fold increase in predictive ability (PA) compared to GS for traditionally estimated FDK (FDK_V). Next, the AI‐based FDK was evaluated along with other traits in multi‐trait (MT) GS models to predict DON. The inclusion of FDK_QNIR and FDK_QVIS with days to heading as covariates improved the PA for DON by 58% over the baseline single‐trait GS model. We next used hyperspectral imaging of FHB‐infected wheat kernels as a novel avenue to improve the MT GS for DON. The PA for DON using selected wavebands derived from hyperspectral imaging in MT GS models surpassed the single‐trait GS model by around 40%. Finally, we evaluated phenomic prediction for DON by integrating hyperspectral imaging with deep learning to directly predict DON in FHB‐infected wheat kernels and observed an accuracy (R2 = 0.45) comparable to best‐performing MT GS models. This study demonstrates the potential application of AI and vision‐based platforms to improve PA for FHB‐related traits using genomic and phenomic selection. Core Ideas Vision and artificial intelligence (AI)‐based technology provide an effective way to phenotype Fusarium‐damaged kernels (FDK) in wheat. Inclusion of AI‐based FDK as a covariate in multi‐trait genomic prediction models yields high predictive ability for deoxynivalenol (DON). Hyperspectral imaging can be leveraged to improve the predictive ability of DON using genomic prediction as well as for direct phenomic prediction. Plain Language Summary Fusarium head blight (FHB) is a devastating disease of wheat and breeding for resistant cultivars is the best approach to counter this disease. However, complex phenotyping of various FHB traits makes it harder for breeders to select resistant cultivars. Our study investigates the usefulness of artificial intelligence (AI)‐based phenotyping in improving the prediction accuracy (PA) of FHB traits in wheat. We demonstrate that AI‐derived Fusarium‐damaged kernels phenotype can improve the prediction of FHB traits using genomic selection. Furthermore, we explored novel tools like hyperspectral imaging and deep learning for improved prediction of FHB resistance in wheat. Our results suggest that the application of novel technologies can be very useful in improving the prediction of FHB traits and can assist wheat breeders in developing FHB‐resistant cultivars.

Fusarium head blight (FHB) is one of the most destructive fungal diseases affecting wheat (Triticum aestivum). Moreover, it is notorious for producing mycotoxin deoxynivalenol (DON), posing a significant global threat to food and feed safety. Traditional methods like enzyme‐linked immunosorbent assay (ELISA) and gas chromatography‐mass spectrometry (GC‐MS) are commonly used to assess DON levels in grain or flour samples and are time‐consuming and expensive. Therefore, a faster, cost‐effective method to estimate DON content is needed, especially for enhancing breeding efforts to reduce DON levels in wheat. In this study, we envisioned integrating close‐range hyperspectral imaging with deep learning (DL) models to estimate DON content in wheat meal/flour. We selected 243 advanced breeding lines from the South Dakota State University (SDSU) wheat breeding program that were evaluated in FHB nurseries (2019–2020 and 2020–2021). The wheat meal samples were analyzed for DON content using GC‐MS and subsequently subjected to close‐range hyperspectral imaging. We evaluated three conventional machine learning (ML), two DL models and data augmentation. Among the conventional ML models, partial least squares regression (PLSR) (with R2P = 0.88 and 0.90 for original and augmented datasets, respectively) demonstrated the highest prediction accuracies for DON content. However, the one‐dimensional convolutional neural network (1D‐CNN) achieved the highest prediction accuracies (R2P = 0.90 and = 0.96 for original and augmented datasets, respectively) compared to all tested models and demonstrated the lowest error. In conclusion, integration of advanced hyperspectral imaging with ML approaches exhibits significant potential for high‐throughput and cost‐effective estimation of DON content in wheat, thereby accelerating wheat breeding efforts for reduced DON levels. Core Ideas Close‐range hyperspectral imaging coupled with machine learning provides a novel avenue for predicting DON in wheat. Deep learning models performed better than conventional machine learning methods for rapid DON prediction. Data augmentation can be successfully used to evaluate machine learning models when using smaller image datasets.

Human papillomaviruses (HPVs) are small double-stranded DNA viruses responsible for a significant burden of disease, including cervical, oropharyngeal and other anogenital cancers. The initiation of HPV DNA replication depends on the cooperative function of the viral E1 helicase and the E2 initiation protein. E1 assembles as a hexameric helicase that unwinds the viral genome, while E2 facilitates E1 recruitment to the replication origin and modulates its enzymatic functions. In this study, full-length HPV16 E1 helicase was successfully expressed and purified to homogeneity. Biochemical analyses confirmed that the purified protein forms oligomeric complexes consistent with its helicase function and exhibits ATPase activity that is significantly stimulated in the presence of single-stranded DNA. Helicase assays demonstrated DNA unwinding activity dependent on ATP hydrolysis.Electrophoretic mobility shift assays revealed that stable E1–E2–DNA complexes require the presence of E2, while the putative E1 binding site within the viral origin is dispensable, for this complex assembly. E2 protein facilitated the E1 recruitment, indicating that E2 plays a primary role in complex assembly. Furthermore, E2 was observed to inhibit both the ATPase and helicase activities of E1, suggesting a regulatory mechanism that controls the origin unwinding.Quantitative characterization of the E1–E2 interaction was performed using bio-layer interferometry. The binding studies demonstrated that E1 and E2 form a high-affinity complex with nanomolar dissociation constants. The presence of ATP or ADP promoted dissociation of the complex, whereas single-stranded DNA had minimal impact on nucleotide-induced dissociation. Additional experiments showed that critical glutamic acid residues in E2 (E20 and E39) severely impaired E1 binding, confirming their importance in maintaining complex stability.These findings provide new insights into the mechanistic basis of HPV DNA replication initiation. The work establishes that E2 is both an essential factor for recruiting E1 to the origin and a negative regulator of its enzymatic activities. Moreover, the results highlight the dynamic modulation of E1–E2 interactions by nucleotide cofactors. Together, this study advances the understanding of papillomavirus replication and offers a foundation for future efforts aimed at targeting viral replication processes therapeutically.

Human papillomavirus (HPV) is a group of alpha papillomaviruses that cause various illnesses, including cancer. There are more than 160 types of HPV, with many being “high-risk” types that have been clinically linked to cervical and other types of cancer. “Low-risk” types of HPV cause less severe conditions, such as genital warts. Over the past few decades, numerous studies have shed light on how HPV induces carcinogenesis. The HPV genome is a circular double-stranded DNA molecule that is approximately 8 kilobases in size. Replication of this genome is strictly regulated and requires two virus-encoded proteins, E1 and E2. E1 is a DNA helicase that is necessary for replisome assembly and replication of the HPV genome. On the other hand, E2 is responsible for initiating DNA replication and regulating the transcription of HPV-encoded genes, most importantly the E6 and E7 oncogenes. This article explores the genetic characteristics of high-risk HPV types, the roles of HPV-encoded proteins in HPV DNA replication, the regulation of transcription of E6 and E7 oncogenes, and the development of oncogenesis.

Store-operated Ca 2+ entry (SOCE), the fundamental Ca 2+ signaling mechanism in myogenesis, is mediated by stromal interaction molecule (STIM), which senses the depletion of endoplasmic reticulum Ca 2+ stores and induces Ca 2+ influx by activating Orai channels in the plasma membrane. Recently, STIM2β, an eight-residue-inserted splice variant of STIM2, was found to act as an inhibitor of SOCE. Although a previous study demonstrated an increase in STIM2β splicing during in vitro differentiation of skeletal muscle, the underlying mechanism and detailed function of STIM2β in myogenesis remain unclear. In this study, we investigated the function of STIM2β in myogenesis using the C2C12 cell line with RNA interference-mediated knockdown and CRISPR-Cas-mediated knockout approaches. Deletion of STIM2β delayed myogenic differentiation through the MEF2C and NFAT4 pathway in C2C12 cells. Further, loss of STIM2β increased cell proliferation by altering Ca 2+ homeostasis and inhibited cell cycle arrest mediated by the cyclin D1-CDK4 degradation pathway. Thus, this study identified a previously unknown function of STIM2β in myogenesis and improves the understanding of how cells effectively regulate the development process via alternative splicing.

Genetic dissection of yield component traits including spike and kernel characteristics is essential for the continuous improvement in wheat yield. Genome-wide association studies (GWAS) have been frequently used to identify genetic determinants for spike and kernel-related traits in wheat, though none have been employed in hard winter wheat (HWW) which represents a major class in US wheat acreage. Further, most of these studies relied on assembled diversity panels instead of adapted breeding lines, limiting the transferability of results to practical wheat breeding. Here we assembled a population of advanced/elite breeding lines and well-adapted cultivars and evaluated over four environments for phenotypic analysis of spike and kernel traits. GWAS identified 17 significant multi-environment marker–trait associations (MTAs) for various traits, representing 12 putative quantitative trait loci (QTLs), with five QTLs affecting multiple traits. Four of these QTLs mapped on three chromosomes 1A, 5B, and 7A for spike length, number of spikelets per spike (NSPS), and kernel length are likely novel. Further, a highly significant QTL was detected on chromosome 7AS that has not been previously associated with NSPS and putative candidate genes were identified in this region. The allelic frequencies of important quantitative trait nucleotides (QTNs) were deduced in a larger set of 1,124 accessions which revealed the importance of identified MTAs in the US HWW breeding programs. The results from this study could be directly used by the breeders to select the lines with favorable alleles for making crosses, and reported markers will facilitate marker-assisted selection of stable QTLs for yield components in wheat breeding.

Obsessive compulsive disorder (OCD) is a common psychiatric disorder which is easily recognized. However, sometimes patients of OCD present in such an atypical or bizarre way that their problem comes to notice as being a psychiatric disorder after multiple consultations in different specialties. We are reporting a case of a man who had first sought opinion in the Department of Ear, Nose and Throat (ENT) for hearing impairment. He was then referred to a neurologist and a general physician for evaluation of neurological cause of his symptom. As no pathology related to ENT or neurology could be detected, he was referred to the Department of Psychiatry. The patient's chief complaints were difficulty in hearing and inability to understand at once. He could be diagnosed as a case of OCD after meticulous evaluation and studying his response to treatment. There was significant improvement in all the presenting symptoms over a period of 6 weeks on 60 mg of fluoxetine.