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IntroductionCrop height and above-ground biomass (AGB) serve as crucial indicators for monitoring crop growth and estimating grain yield. Timely and accurate acquisition of wheat crop height and AGB data is paramount for guiding agricultural production. However, traditional data acquisition methods suffer from drawbacks such as time-consuming, laborious and destructive sampling.MethodsThe current approach to estimating AGB using unmanned aerial vehicles (UAVs) remote sensing relies solely on spectral data, resulting in low accuracy in estimation. This method fails to address the ill-posed inverse problem of mapping from two-dimensional to three-dimensional and issues related to spectral saturation. To overcome these challenges, RGB and multispectral sensors mounted on UAVs were employed to acquire spectral image data. The five-directional oblique photography technique was utilized to construct the three-dimensional point cloud for extracting crop height.Results and DiscussionThis study comparatively analyzed the potential of the mean method and the Accumulated Incremental Height (AIH) method in crop height extraction. Utilizing Vegetation Indices (VIs), AIH and their feature combinations, models including Random Forest Regression (RFR), eXtreme Gradient Boosting (XGBoost), Gradient Boosting Regression Trees (GBRT), Support Vector Regression (SVR) and Ridge Regression (RR) were constructed to estimate winter wheat AGB. The research results indicated that the AIH method performed well in crop height extraction, with minimal differences between 95% AIH and measured crop height values were observed across various growth stages of wheat, yielding R2 ranging from 0.768 to 0.784. Compared to individual features, the combination of multiple features significantly improved the model’s estimate accuracy. The incorporation of AIH features helps alleviate the effects of spectral saturation. Coupling VIs with AIH features, the model’s R2 increases from 0.694-0.885 with only VIs features to 0.728-0.925. In comparing the performance of five machine learning algorithms, it was discovered that models constructed based on decision trees were superior to other machine learning algorithms. Among them, the RFR algorithm performed optimally, with R2 ranging from 0.9 to 0.93.ConclusionIn conclusion, leveraging multi-source remote sensing data from UAVs with machine learning algorithms overcomes the limitations of traditional crop monitoring methods, offering a technological reference for precision agriculture management and decision-making.

Abstract As the world's first oral nuclear export inhibitor, selinexor is increasingly being used in clinical applications for malignant tumors. However, there is no extensive exploration on selinexor's adverse events (ADEs), necessitating a real-word assessment of its clinical medication safety. FAERS data (July 2019–June 2023) were searched for selinexor ADE reports across all indications. Use the system organ class (SOC) and preferred terms (PT) from the medical dictionary for regulatory activities (MedDRA) to describe, categorize, and statistic ADEs. Disproportionality analysis was employed through calculation of reporting odds ratio (ROR) and proportional reporting ratio (PRR). Based on total of 4392 selinexor related ADE reports as the primary suspect (PS), of which 2595 instances were severe outcomes. The predominant ADEs included gastrointestinal disorders, myelosuppression symptoms, and various nonspecific manifestations. 124 signals associated with selinexor ADE were detected, and 10 of these top 15 signals were not included into the instructions. Our study provides real-world evidence regarding the drug safety of selinexor, which is crucial for clinicians to safeguard patients’ health.

Summary: Background: The E1A-associated protein p300 (p300) has emerged as a promising target for cancer therapy due to its crucial role in promoting oncogenic signaling pathways in various cancers, including prostate cancer. This need is particularly significant in prostate cancer. While androgen deprivation therapy (ADT) has demonstrated promising efficacy in prostate cancer, its long-term use can eventually lead to the development of castration-resistant prostate cancer (CRPC) and neuroendocrine prostate cancer (NEPC). Notably, p300 has been identified as an important co-activator of the androgen receptor (AR), highlighting its significance in prostate cancer progression. Moreover, recent studies have revealed the involvement of p300 in AR-independent oncogenes associated with NEPC. Therefore, the blockade of p300 may emerge as an effective therapeutic strategy to address the challenges posed by both CRPC and NEPC. Methods: We employed AI-assisted design to develop a peptide-based PROTAC (proteolysis-targeting chimera) drug that targets p300, effectively degrading p300 in vitro and in vivo utilizing nano-selenium as a peptide drug delivery system. Findings: Our p300-targeting peptide PROTAC drug demonstrated effective p300 degradation and cancer cell-killing capabilities in both CRPC, AR-negative, and NEPC cells. This study demonstrated the efficacy of a p300-targeting drug in NEPC cells. In both AR-positive and AR-negative mouse models, the p300 PROTAC drug showed potent p300 degradation and tumor suppression. Interpretation: The design of peptide PROTAC drug targeting p300 is feasible and represents an efficient therapeutic strategy for CRPC, AR-negative prostate cancer, and NEPC. Funding: The funding details can be found in the Acknowledgements section.

Summary: Background: Finding the oncogene, which was able to inhibit tumor cells intrinsically and improve the immune answers, will be the future direction for renal cancer combined treatment. Following patient sample analysis and signaling pathway examination, we propose p21-activated kinase 4 (PAK4) as a potential target drug for kidney cancer. PAK4 exhibits high expression levels in patient samples and plays a regulatory role in the immune microenvironment. Methods: Utilizing AI software for peptide drug design, we have engineered a specialized peptide proteolysis targeting chimera (PROTAC) drug with selectivity for PAK4. To address challenges related to drug delivery, we developed a nano-selenium delivery system for efficient transport of the peptide PROTAC drug, termed PpD (PAK4 peptide degrader). Findings: We successfully designed a peptide PROTAC drug targeting PAK4. PpD effectively degraded PAK4 with high selectivity, avoiding interference with other homologous proteins. PpD significantly attenuated renal carcinoma proliferation in vitro and in vivo. Notably, PpD demonstrated a significant inhibitory effect on tumor proliferation in a fully immunocompetent mouse model, concomitantly enhancing the immune cell response. Moreover, PpD demonstrated promising tumor growth inhibitory effects in mini-PDX and PDO models, further underscoring its potential for clinical application. Interpretation: This PAK4-targeting peptide PROTAC drug not only curtails renal cancer cell proliferation but also improves the immune microenvironment and enhances immune response. Our study paves the way for innovative targeted therapies in the management of renal cancer. Funding: This work is supported by Research grants from non-profit organizations, as stated in the Acknowledgments.

Abstract CDK4/6 inhibitors (CDK4/6i) show anticancer activity in certain human malignancies, such as breast cancer. However, their application to other tumor types and intrinsic resistance mechanisms are still unclear. Here, we demonstrate that MYC amplification confers resistance to CDK4/6i in bladder, prostate and breast cancer cells. Mechanistically, MYC binds to the promoter of the E3 ubiquitin ligase KLHL42 and enhances its transcription, leading to RB1 deficiency by inducing both phosphorylated and total pRB1 ubiquitination and degradation. We identify a compound that degrades MYC, A80.2HCl, which induces MYC degradation at nanomolar concentrations, restores pRB1 protein levels and re-establish sensitivity of MYC high-expressing cancer cells to CDK4/6i. The combination of CDK4/6i and A80.2HCl result in marked regression in tumor growth in vivo. Altogether, these results reveal the molecular mechanisms underlying MYC-induced resistance to CDK4/6i and suggest the utilization of the MYC degrading molecule A80.2HCl to potentiate the therapeutic efficacy of CDK4/6i.

Casein is among the most abundant proteins in milk and has high nutritional value. Casein’s interactions with polysaccharides, polyphenols, and metal ions are important for regulating the functional properties and textural quality of dairy foods. To improve the functional properties of casein-based foods, a deep understanding of the interaction mechanisms and the influencing factors between casein and other food components is required. This review started by elucidating the interaction mechanism of casein with polysaccharides, polyphenols, and metal ions. Thermodynamic incompatibility and attraction are the fundamental factors in determining the interaction types between casein and polysaccharides, which leads to different phase behaviors and microstructural types in casein-based foods. Additionally, the interaction of casein with polyphenols primarily occurs through non-covalent (hydrogen bonding, hydrophobic interactions, van der Waals forces, and ionic bonding) or covalent interaction (primarily based on the oxidation of proteins or polyphenols by enzymatic or non-enzymatic (alkaline or free radical grafting) approaches). Moreover, the selectivity of casein to specific metal ions is also introduced. Factors affecting the binding of casein to the above three components, such as temperature, pH, the mixing ratio, and the fine structure of these components, are also summarized to provide a good foundation for casein-based food applications.

Due to their low density, high specific damping capacity and high shock absorbency, magnesium (Mg) alloys have great potential for development as high-performance lightweight armor materials in industrial applications. However, their applications are still limited owing to low strength, ductility and formability. Gradient structure design has been shown to be a good method for improving the mechanical properties and ballistic resistance of Mg alloy plate. This work aims to thoroughly reveal the root causes of ballistic performance enhancement of gradient structured (GS) Mg alloy armor material through ballistic tests and finite element simulations. Compared with homogeneous Mg alloy plate of the same dimensions, the impact energy absorption of GS plate is increased by about 40%. The enhanced strength and plasticity from the gradient structure design certainly contribute in part to the ballistic resistance. More importantly, the gradient structure results in a transition of failure modes from the typical petal-shaped dehiscence to delamination and shear fracture. Based on detailed finite element analysis, we deeply understand the deformation process and the effect of the gradient structure on the propagation of stress wave during ballistic impacting. The energy absorption by each defeat mechanism is also theoretically calculated to quantitatively interpret their intrinsic effects. Meanwhile, microstructural observations and fracture morphology have demonstrated the appearance of adiabatic shear bands along the boundary of the cylindrical plunger after ballistic perforation of GS plate. Therefore, the failure mode transition caused by gradient structure design must also play a major role in improving the ballistic resistance.

Accurately assessing maize crop height (CH) and aboveground biomass (AGB) is crucial for understanding crop growth and light-use efficiency. Unmanned aerial vehicle (UAV) remote sensing, with its flexibility and high spatiotemporal resolution, has been widely applied in crop phenotyping studies. Traditional canopy height models (CHMs) are significantly influenced by image resolution and meteorological factors. In contrast, the accumulated incremental height (AIH) extracted from point cloud data offers a more accurate estimation of CH. In this study, vegetation indices and structural features were extracted from optical imagery, nadir and oblique photography, and LiDAR point cloud data. Optuna-optimized models, including random forest regression (RFR), light gradient boosting machine (LightGBM), gradient boosting decision tree (GBDT), and support vector regression (SVR), were employed to estimate maize AGB. Results show that AIH99 has higher accuracy in estimating CH. LiDAR demonstrated the highest accuracy, while oblique photography and nadir photography point clouds were slightly less accurate. Fusion of multi-source data achieved higher estimation accuracy than single-sensor data. Embedding structural features can mitigate spectral saturation, with R2 ranging from 0.704 to 0.939 and RMSE ranging from 0.338 to 1.899 t/hm2. During the entire growth cycle, the R2 for LightGBM and RFR were 0.887 and 0.878, with an RMSE of 1.75 and 1.76 t/hm2. LightGBM and RFR also performed well across different growth stages, while SVR showed the poorest performance. As the amount of nitrogen application gradually decreases, the accumulation and accumulation rate of AGB also gradually decrease. This high-throughput crop-phenotyping analysis method offers advantages, such as speed and high accuracy, providing valuable references for precision agriculture management in maize fields.

The timely and precise prediction of winter wheat yield plays a critical role in understanding food supply dynamics and ensuring global food security. In recent years, the application of unmanned aerial remote sensing has significantly advanced agricultural yield prediction research. This has led to the emergence of numerous vegetation indices that are sensitive to yield variations. However, not all of these vegetation indices are universally suitable for predicting yields across different environments and crop types. Consequently, the process of feature selection for vegetation index sets becomes essential to enhance the performance of yield prediction models. This study aims to develop an integrated feature selection method known as PCRF-RFE, with a focus on vegetation index feature selection. Initially, building upon prior research, we acquired multispectral images during the flowering and grain filling stages and identified 35 yield-sensitive multispectral indices. We then applied the Pearson correlation coefficient (PC) and random forest importance (RF) methods to select relevant features for the vegetation index set. Feature filtering thresholds were set at 0.53 and 1.9 for the respective methods. The union set of features selected by both methods was used for recursive feature elimination (RFE), ultimately yielding the optimal subset of features for constructing Cubist and Recurrent Neural Network (RNN) yield prediction models. The results of this study demonstrate that the Cubist model, constructed using the optimal subset of features obtained through the integrated feature selection method (PCRF-RFE), consistently outperformed the RNN model. It exhibited the highest accuracy during both the flowering and grain filling stages, surpassing models constructed using all features or subsets derived from a single feature selection method. This confirms the efficacy of the PCRF-RFE method and offers valuable insights and references for future research in the realms of feature selection and yield prediction studies.

The retention of 53BP1 at DNA double strand breaks (DSBs) is inhibitory to homologous recombination repair. Following ionising radiation, the authors demonstrate that TRABID-mediated deubiquitination of 53BP1 promotes its retention, sensitising prostate cancer to PARP inhibition.

Abstract The mechanistic (formally “mammalian”) target of rapamycin (mTOR) pathway serves as a crucial regulator of various biological processes such as cell growth and cancer progression. In bladder cancer, recent discoveries showing the cancer-promoting role of mTOR complex 1 have attracted wide attention. However, the regulation of mTOR signaling in bladder cancer is complicated and the underlying mechanism remains elusive. Here, we report that the deubiquitinating enzyme, ovarian tumor domain-containing protein 5 (OTUD5), can activate the mTOR signaling pathway, promote cancer progression, and show its oncogenic potential in bladder cancer. In our study, we found that OTUD5 deubiquitinated a RING-type E3 ligase, RNF186, and stabilized its function. In addition, the stabilization of RNF186 further led to the degradation of sestrin2, which is an inhibitor of the mTOR signaling pathway. Together, we provide novel insights into the pathogenesis of bladder cancer and first prove that OTUD5 can promote bladder cancer progression through the OTUD5-RNF186-sestrin2-mTOR axis, which may be exploited in the future for the diagnosis and treatment of this malignancy.

Abstract Androgen receptor splice variant‐7 (AR‐V7), one of the major driving factors, is the most attractive drug target in castration‐resistant prostate cancer (CRPC). Currently, no available drugs efficiently target AR‐V7 in clinical practice. The DNA binding domain (DBD) is indispensable for the transcriptional activity of AR full length and AR splice variants, including AR‐V7. Based on the homodimerization structure of the AR DBD, a novel peptide‐based proteolysis‐targeting chimera (PROTAC) drug is designed to induce AR and AR‐V7 degradation in a DBD and MDM2‐dependent manner, without showing any activity on other hormone receptors. To overcome the short half‐life and poor cell penetrability of peptide PROTAC drugs, an ultrasmall gold (Au)‐peptide complex platform to deliver the AR DBD PROTAC in vivo is developed. The obtained Au‐AR pep‐PROTAC effectively degrades AR and AR‐V7 in prostate cancer cell lines, particularly in CWR22Rv1 cells with DC50 values 48.8 and 79.2 nM, respectively. Au‐AR pep‐PROTAC results in suppression of AR levels and induces tumor regression in both enzalutamide sensitive and resistant prostate cancer animal models. Further optimization of the Au‐AR pep‐PROTAC can ultimately lead to a new therapy for AR‐V7‐positive CRPC.

Above-ground biomass (AGB) serves as an indicator of crop growth status, and acquiring timely AGB information is crucial for estimating crop yield and determining appropriate water and fertilizer inputs. Unmanned Aerial Vehicles (UAVs) equipped with RGB cameras offer an affordable and practical solution for efficiently obtaining crop AGB. However, traditional vegetation indices (VIs) alone are insufficient in capturing crop canopy structure, leading to poor estimation accuracy. Moreover, different flight heights and machine learning algorithms can impact estimation accuracy. Therefore, this study aims to enhance wheat AGB estimation accuracy by combining VIs, crop height, and texture features while investigating the influence of flight height and machine learning algorithms on estimation. During the heading and grain-filling stages of wheat, wheat AGB data and UAV RGB images were collected at flight heights of 30 m, 60 m, and 90 m. Machine learning algorithms, including Random Forest Regression (RFR), Gradient Boosting Regression Trees (GBRT), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (Lasso) and Support Vector Regression (SVR), were utilized to construct wheat AGB estimation models. The research findings are as follows: (1) Estimation accuracy using VIs alone is relatively low, with R2 values ranging from 0.519 to 0.695. However, combining VIs with crop height and texture features improves estimation accuracy, with R2 values reaching 0.845 to 0.852. (2) Estimation accuracy gradually decreases with increasing flight height, resulting in R2 values of 0.519–0.852, 0.438–0.837, and 0.445–0.827 for flight heights of 30 m, 60 m, and 90 m, respectively. (3) The choice of machine learning algorithm significantly influences estimation accuracy, with RFR outperforming other machine learnings. In conclusion, UAV RGB images contain valuable crop canopy information, and effectively utilizing this information in conjunction with machine learning algorithms enables accurate wheat AGB estimation, providing a new approach for precision agriculture management using UAV remote sensing technology.

Abstract The Bcr/Abl plays a central role in Philadelphia chromosome‐positive (Ph+) leukemia because of the constitutively activated Abl tyrosine kinase and its downstream pathways. Currently, the clinical treatment of imatinib‐resistant patients with tyrosine kinase inhibitors is severely limited by drug resistance and adverse effects. Herein, a dual‐targeting proteolysis‐targeting chimera (PROTAC) protein drug, termed PMIBcr/Abl‐R6, is designed by engrafting an MDM2/p53 inhibition peptide sequence onto the Bcr/Abl tetramerization domain. PMIBcr/Abl‐R6, harboring a Bcr/Abl targeting sequence and an MDM2 binding sequence, acts as a PROTAC drug in Ph+ leukemia cells. Its dual‐targeting constitution suggests that PMIBcr/Abl‐R6 designs to target the tetramerization domain instead of the Abl kinase domain, therefore has the potential to overcome drug resistance mutations in the kinase domain. The efficient ability of PMIBcr/Abl‐R6 is demonstrated to simultaneously induce Bcr/Abl degradation and activate the p53 pathway. PMIBcr/Abl‐R6 has the potential to overcome drug resistance in Ph+ leukemias by multiple mechanisms.

Purpose: Radioactive iodine (RAI) is used to treat thyroid cancer patients with a clear paradigm for most patients. End-stage renal disease (ESRD) patients pose several challenges when undergoing RAI treatment, primarily due to the lack of renal clearance. We retrospectively report our experience with RAI treatment in a cohort of patients with ESRD and provide a set of recommendations on aspects such as the need for adjusted dose activity, balancing scheduling between RAI therapy and dialysis, and radiation safety precautions., Patients and Methods: In this study, we report on 5 patients (6 cases), with ESRD on dialysis, treated with RAI for thyroid cancer. Retention measurements to determine individual biological clearance of RAI from the patient's body before and after dialysis sessions were assessed using external exposure dose rates measured at 1 m., Results: Delayed biological clearance of RAI, after the first hemodialysis session, resulted in a longer RAI effective half-life as a consequence of longer retention periods, consistent with observations reported in scientific literature. To achieve a much closer radiation exposure compared with a nondialysis patient, one would recommend administering ~20%-30% of the dose activity normally administered to a thyroid cancer patient based on their medical history, histopathology, and uptake with the appropriate dialysis schedule., Conclusions: Special precautions should be taken with the administration of RAI in ESRD patients by adjusting the prescribed dose activity, dialysis sessions, and paying special attention to wastes. Pooling data from multiple centers may be useful to build a consensus and substantiated recommendations., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)