Differential Perturbation Research Articles

CPADS: a web tool for comprehensive pancancer analysis of drug sensitivity.

Drug therapy is vital in cancer treatment. Accurate analysis of drug sensitivity for specific cancers can guide healthcare professionals in prescribing drugs, leading to improved patient survival and quality of life. However, there is a lack of web-based tools that offer comprehensive visualization and analysis of pancancer drug sensitivity. We gathered cancer drug sensitivity data from publicly available databases (GEO, TCGA and GDSC) and developed a web tool called Comprehensive Pancancer Analysis of Drug Sensitivity (CPADS) using Shiny. CPADS currently includes transcriptomic data from over 29 000 samples, encompassing 44 types of cancer, 288 drugs and more than 9000 gene perturbations. It allows easy execution of various analyses related to cancer drug sensitivity. With its large sample size and diverse drug range, CPADS offers a range of analysis methods, such as differential gene expression, gene correlation, pathway analysis, drug analysis and gene perturbation analysis. Additionally, it provides several visualization approaches. CPADS significantly aids physicians and researchers in exploring primary and secondary drug resistance at both gene and pathway levels. The integration of drug resistance and gene perturbation data also presents novel perspectives for identifying pivotal genes influencing drug resistance. Access CPADS at https://smuonco.shinyapps.io/CPADS/ or https://robinl-lab.com/CPADS.

Adversarial Robust Safeguard for Evading Deep Facial Manipulation

The non-consensual exploitation of facial manipulation has emerged as a pressing societal concern. In tandem with the identification of such fake content, recent research endeavors have advocated countering manipulation techniques through proactive interventions, specifically the incorporation of adversarial noise to impede the manipulation in advance. Nevertheless, with insufficient consideration of robustness, we show that current methods falter in providing protection after simple perturbations, e.g., blur. In addition, traditional optimization-based methods face limitations in scalability as they struggle to accommodate the substantial expansion of data volume, a consequence of the time-intensive iterative pipeline. To solve these challenges, we propose a learning-based model, Adversarial Robust Safeguard (ARS), to generate desirable protection noise in a single forward process, concurrently exhibiting a heightened resistance against prevalent perturbations. Specifically, our method involves a two-way protection design, characterized by a basic protection component responsible for generating efficacious noise features, coupled with robust protection for further enhancement. In robust protection, we first fuse image features with spatially duplicated noise embedding, thereby accounting for inherent information redundancy. Subsequently, a combination comprising a differentiable perturbation module and an adversarial network is devised to simulate potential information degradation during the training process. To evaluate it, we conduct experiments on four manipulation methods and compare recent works comprehensively. The results of our method exhibit good visual effects with pronounced robustness against varied perturbations at different levels.

A hybrid evolutionary JAYA algorithm based on global optimization for 5G e-commerce logistics

With the increasing demand for logistics in modern society, how to achieve low-cost and efficient logistics delivery has become an urgent research topic. A hybrid evolutionary JAYA algorithm (H-JAYA) based on global optimization was designed to address the complex path planning problem of electric vehicles. This algorithm introduces a reverse learning mechanism to calculate the current optimal and worst individuals, while using differential perturbation mechanism and sine cosine operator to update the individual’s position. In addition, the study used the H-JAYA algorithm to construct a corresponding mathematical model for the optimization problem of electric vehicle paths. The results showed that in the three examples, the H-JAYA algorithm tested the optimal curve convergence speed, and it tended to stabilize after about 30 iterations. Meanwhile, in the RCDP5001 example, the total cost of the H-JAYA algorithm reached the lowest value of 623 yuan. The H-JAYA algorithm has significant advantages in solving the distribution path problem of electric vehicles, and can be well applied to practical logistics distribution, providing effective technical support for modern e-commerce logistics planning.

Eutectic Resolves Lysolipid Paradox in Thermoresponsive Liposomes.

A better molecular understanding of the temperature-triggered drug release from lysolipid-based thermosensitive liposomes (LTSLs) is needed to overcome the recent setbacks in developing this important drug delivery system. Enhanced drug release was previously rationalized in terms of detergent-like effects of the lysolipid monostearyl lysophosphatidylcholine (MSPC), stabilizing local membrane defects upon LTSL lipid melting. This is highly surprising and here referred to as the 'lysolipid paradox,' because detergents usually induce the opposite effect─they cause leakage upon freezing, not melting. Here, we aim at better answers to (i) why lysolipid does not compromise drug retention upon storage of LTSLs in the gel phase, (ii) how lysolipids can enhance drug release from LTSLs upon lipid melting, and (iii) why LTSLs typically anneal after some time so that not all drug gets released. To this end, we studied the phase transitions of mixtures of dipalmitoylphosphatidylcholine (DPPC) and MSPC by a combination of differential scanning and pressure perturbation calorimetry and identified the phase structures with small- and wide-angle X-ray scattering (SAXS and WAXS). The key result is that LTSLs, which contain the standard amount of 10 mol % MSPC, are at a eutectic point when they release their cargo upon melting at about 41 °C. The eutectic present below 41 °C consists of a MSPC-depleted gel phase as well as small domains of a hydrocarbon chain interdigitated gel phase containing some 30 mol % MSPC. In these interdigitated domains, the lysolipid is stored safely without compromising membrane integrity. At the eutectic temperature, both the MSPC-depleted bilayer and interdigitated MSPC-rich domains melt at once to fluid bilayers, respectively. Intact, fluid membranes tolerate much less MSPC than interdigitated domains─where the latter have melted, the high local MSPC content causes transient pores. These pores allow for fast drug release. However, these pores disappear, and the membrane seals again as the MSPC distributes more evenly over the membrane so that its local concentration decreases below the pore-stabilizing threshold. We provide a pseudobinary phase diagram of the DPPC-MSPC system and structural and volumetric data for the interdigitated phase.

Alloy informatics through ab initio charge density profiles: Case study of hydrogen effects in face-centred cubic crystals

Materials design has traditionally evolved through trial-error approaches, mainly due to the non-local relationship between microstructures and properties such as strength and toughness. We propose ‘alloy informatics’ as a machine learning based prototype predictive approach for alloys and compounds, using electron charge density profiles derived from first-principle calculations. We demonstrate this framework in the case of hydrogen interstitials in face-centred cubic crystals, showing that their differential electron charge density profiles capture crystal properties and defect-crystal interaction properties. Radial Distribution Functions (RDFs) of defect-induced differential charge density perturbations highlight the resulting screening effect, and, together with hydrogen Bader charges, strongly correlate to a large set of atomic properties of the metal species forming the bulk crystal. We observe the spontaneous emergence of classes of charge responses while coarse-graining over crystal compositions. Nudge-Elastic-Band calculations show that RDFs and charge features also connect to hydrogen migration energy barriers between interstitial sites. Unsupervised machine-learning on RDFs supports classification, unveiling compositional and configurational non-localities in the similarities of the perturbed densities. Electron charge density perturbations may be considered as bias-free descriptors for a large variety of defects.

Research and implementation of fault data recovery method for dry-type transformer temperature control sensor based on ISSA-LSTM algorithm

This study proposes a temperature control sensor fault data recovery model based on the improved Sparrow Search Algorithm (ISSA) optimizing Long Short-Term Memory (LSTM) to address erroneous data output caused by temperature monitoring sensor failures in dry-type transformers. The model aims to recover faulty sensor data in dynamic processes. It enhances the algorithm by initializing the population using Tent chaotic mapping, employing t-distribution and differential variation perturbations, and introducing a dynamic step size factor. Simulation experiments evaluated the performance of ISSA-LSTM, SSA-LSTM, PSO-LSTM, GA-LSTM, and ACSA-LSTM algorithms. The ISSA-LSTM model outperforms the worst PSO-LSTM model by 82.6%, 82.1%, and 1.8% in terms of MAE, RMSE and R2 values, respectively. Field experiments confirmed the algorithm's effectiveness in recovering data from faulty sensors and different fault types, improving the accuracy and stability of temperature control sensor fault data recovery in dry-type transformers.

Fecal microbiota transplantation unveils sex-specific differences in a controlled cortical impact injury mouse model.

Contusion type of traumatic brain injury (TBI) is a major cause of locomotor disability and mortality worldwide. While post-TBI deleterious consequences are influenced by gender and gut dysbiosis, the sex-specific importance of commensal gut microbiota is underexplored after TBI. In this study, we investigated the impact of controlled cortical impact (CCI) injury on gut microbiota signature in a sex-specific manner in mice. We depleted the gut microflora of male and female C57BL/6 mice using antibiotic treatment. Thereafter, male mice were colonized by the gut microbiota of female mice and vice versa, employing the fecal microbiota transplantation (FMT) method. CCI surgery was executed using a stereotaxic impactor (Impact One™). For the 16S rRNA gene amplicon study, fecal boli of mice were collected at 3 days post-CCI (dpi). CCI-operated male and female mice exhibited a significant alteration in the genera of Akkermansia, Alistipes, Bacteroides, Clostridium, Lactobacillus, Prevotella, and Ruminococcus. At the species level, less abundance of Lactobacillus helveticus and Lactobacillus hamsteri was observed in female mice, implicating the importance of sex-specific bacteriotherapy in CCI-induced neurological deficits. FMT from female donor mice to male mice displayed an increase in genera of Alistipes, Lactobacillus, and Ruminococcus and species of Bacteroides acidifaciens and Ruminococcus gnavus. Female FMT-recipient mice from male donors showed an upsurge in the genus Lactobacillus and species of Lactobacillus helveticus, Lactobacillus hamsteri, and Prevotella copri. These results suggest that the post-CCI neurological complications may be influenced by the differential gut microbiota perturbation in male and female mice.

A Multi-strategy Enhanced Arithmetic Optimization Algorithm and Its Application in Path Planning of Mobile Robots

A multi-strategy enhanced arithmetic optimization algorithm called MSEAOA is proposed to address the issues of low population diversity, imbalanced exploration and exploitation capabilities, and low accuracy of optimal solution in the Arithmetic Optimization Algorithm. Firstly, using the good point set strategy for population initialization to improve population diversity and thus accelerate convergence speed. Secondly, we integrate the exploration and exploition capabilities of differential self-learning strategy, best example learning strategy, and second-order differential perturbation strategy balancing algorithm. Finally, the introduction of somersault foraging strategy improves the accuracy of the optimal solution. We select 14 classical benchmark test functions and the CEC2019 function test set to test the optimization ability of MSEAOA, and apply MSEAOA to the path planning problem of mobile robots. MSEAOA is compared with other meta-heuristic optimization algorithms, and the experimental results are statistically analyzed by the Wilcoxon rank-sum test. The simulation experimental results show that MSEAOA performs the best among 14 benchmark functions, but for 10 CEC2019 functions, MSEAOA has the best optimization performance among 5 of them (50%). In the path optimization problem of mobile robots, the path obtained by MSEAOA is also the best among all algorithms, its path shortening rate exceeds 8.8% in 83% of environments. The results indicate that MSEAOA is a reliable algorithm suitable for function optimization and practical optimization problems.

Privacy-Preserving Individual-Level COVID-19 Infection Prediction via Federated Graph Learning

Accurately predicting individual-level infection state is of great value since its essential role in reducing the damage of the epidemic. However, there exists an inescapable risk of privacy leakage in the fine-grained user mobility trajectories required by individual-level infection prediction. In this article, we focus on developing a framework of privacy-preserving individual-level infection prediction based on federated learning (FL) and graph neural networks (GNN). We propose Falcon , a F ederated gr A ph L earning method for privacy-preserving individual-level infe C tion predicti ON . It utilizes a novel hypergraph structure with spatio-temporal hyperedges to describe the complex interactions between individuals and locations in the contagion process. By organically combining the FL framework with hypergraph neural networks, the information propagation process of the graph machine learning is able to be divided into two stages distributed on the server and the clients, respectively, so as to effectively protect user privacy while transmitting high-level information. Furthermore, it elaborately designs a differential privacy perturbation mechanism as well as a plausible pseudo location generation approach to preserve user privacy in the graph structure. Besides, it introduces a cooperative coupling mechanism between the individual-level prediction model and an additional region-level model to mitigate the detrimental impacts caused by the injected obfuscation mechanisms. Extensive experimental results show that our methodology outperforms state-of-the-art algorithms and is able to protect user privacy against actual privacy attacks. Our code and datasets are available at the link: https://github.com/wjfu99/FL-epidemic .

Concepts in B-cell Acute Lymphoblastic Leukemia Pathogenesis.

B-cell acute lymphoblastic leukemia (B-ALL) arises from genetic alterations impacting B-cell progenitors, ultimately leading to clinically overt disease. Extensive collaborative efforts in basic and clinical research have significantly improved patient prognoses. Nevertheless, a subset of patients demonstrate resistance to conventional chemotherapeutic approaches and emerging immunotherapeutic interventions. This review highlights the mechanistic underpinnings governing B-ALL transformation. Beginning with exploring normative B-cell lymphopoiesis, we delineate the influence of recurrent germline and somatic genetic aberrations on the perturbation of B-cell progenitor differentiation and pro-tumorigenic signaling, thereby facilitating the neoplastic transformation underlying B-ALL progression. Additionally, we highlight recent advances in the multifaceted landscape of B-ALL, encompassing metabolic reprogramming, microbiome influences, inflammation, and the discernible impact of socioeconomic and racial disparities on B-ALL transformation and patient survival.

Effects of Turbulence‐Induced Blade Position on Flow and Combustion in a Wankel Rotary Engine

Aiming at the problem of low turbulent velocity and incomplete combustion in the combustion process of the Wankel rotary engine (WRE), and setting turbulence‐induced blade (TIB) in the combustion chamber recess is an effective means to enhance the turbulent motion and promote combustion. Carifying the optimal arrangement position of TIB can get better combustion performance. Therefore, a computational fluid dynamics (CFD) simulation model of a WRE with TIB was established in this paper. Based on the differential pressure perturbation mechanism, the influence of the arrangement position of the TIB on the flow field and combustion performance were analyzed. The results showed that when the ratio of blade arrangement distance is less than 0.66, the pressure difference between the leading and trailing of the blade is small. At this time, the in‐cylinder flow is mainly disturbed by the forced disturbance of the TIB, the increase of the turbulent velocity is not obvious, and the diffusion velocity of the flame to the rotor direction is small. When the ratio of blade arrangement distance is greater than 0.66, the pressure difference between the leading and trailing of the blade is large. At this time, the In‐cylinder flow is affected by the pressure difference flow in addition to the forced disturbance of the TIB. The turbulent velocity is effectively enhanced, and the flame velocity is fast to the rotor direction. When the ratio of blade arrangement distance is 0.66, it shows the best combustion performance, and the indicated work is the largest. Compared to the scheme without TIB, the indicated work is increased by 16%.

Local Differential Privacy Based Membership-Privacy-Preserving Federated Learning for Deep-Learning-Driven Remote Sensing

With the development of deep learning, image recognition based on deep learning is now widely used in remote sensing. As we know, the effectiveness of deep learning models significantly benefits from the size and quality of the dataset. However, remote sensing data are often distributed in different parts. They cannot be shared directly for privacy and security reasons, and this has motivated some scholars to apply federated learning (FL) to remote sensing. However, research has found that federated learning is usually vulnerable to white-box membership inference attacks (MIAs), which aim to infer whether a piece of data was participating in model training. In remote sensing, the MIA can lead to the disclosure of sensitive information about the model trainers, such as their location and type, as well as time information about the remote sensing equipment. To solve this issue, we consider embedding local differential privacy (LDP) into FL and propose LDP-Fed. LDP-Fed performs local differential privacy perturbation after properly pruning the uploaded parameters, preventing the central server from obtaining the original local models from the participants. To achieve a trade-off between privacy and model performance, LDP-Fed adds different noise levels to the parameters for various layers of the local models. This paper conducted comprehensive experiments to evaluate the framework’s effectiveness on two remote sensing image datasets and two machine learning benchmark datasets. The results demonstrate that remote sensing image classification models are susceptible to MIAs, and our framework can successfully defend against white-box MIA while achieving an excellent global model.

Deletion of NADPH oxidase 2 in chondrocytes exacerbates ethanol-mediated growth plate disruption in mice without major effects on bone architecture or gene expression.

Excess reactive oxygen species generated by NADPH oxidase 2 (Nox2) in response to ethanol exposure mediate aspects of skeletal toxicity including increased osteoclast differentiation and activity. Because perturbation of chondrocyte differentiation in the growth plate by ethanol could be prevented by dietary antioxidants, we hypothesized that Nox2 in the growth plate was involved in ethanol-associated reductions in longitudinal bone growth. Nox2 conditional knockout mice were generated, where the essential catalytic subunit of Nox2, cytochrome B-245 beta chain (Cybb), is deleted in chondrocytes using a Cre-Lox model with Cre expressed from the collagen 2a1 promoter (Col2a1-Cre). Wild-type and Cre-Lox mice were fed an ethanol Lieber-DeCarli-based diet or pair-fed a control diet for 8 weeks. Ethanol treatment significantly reduced the number of proliferating chondrocytes in the growth plate, enhanced bone marrow adiposity, shortened femurs, reduced body length, reduced cortical bone volume, and decreased mRNA levels of a number of osteoblast and chondrocyte genes. Conditional knockout of Nox2 enzymatic activity in chondrocytes did not consistently prevent any ethanol effects. Rather, knockout mice had fewer proliferating chondrocytes than wild-type mice in both the ethanol- and control-fed animals. Additional analysis of tibia samples from Nox4 knockout mice showed that loss of Nox4 activity also reduced the number of proliferating chondrocytes and altered chondrocyte size in the growth plate. Although Nox enzymatic activity regulates growth plate development, ethanol-associated disruption of the growth plate morphology is independent of ethanol-mediated increases in Nox2 activity.

MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations With Limited Labels.

Semi-supervised learning (SSL) is a promising machine learning paradigm to address the ubiquitous issue of label scarcity in medical imaging. The state-of-the-art SSL methods in image classification utilise consistency regularisation to learn unlabelled predictions which are invariant to input level perturbations. However, image level perturbations violate the cluster assumption in the setting of segmentation. Moreover, existing image level perturbations are hand-crafted which could be sub-optimal. In this paper, we propose MisMatch, a semi-supervised segmentation framework based on the consistency between paired predictions which are derived from two differently learnt morphological feature perturbations. MisMatch consists of an encoder and two decoders. One decoder learns positive attention for foreground on unlabelled data thereby generating dilated features of foreground. The other decoder learns negative attention for foreground on the same unlabelled data thereby generating eroded features of foreground. We normalise the paired predictions of the decoders, along the batch dimension. A consistency regularisation is then applied between the normalised paired predictions of the decoders. We evaluate MisMatch on four different tasks. Firstly, we develop a 2D U-net based MisMatch framework and perform extensive cross-validation on a CT-based pulmonary vessel segmentation task and show that MisMatch statistically outperforms state-of-the-art semi-supervised methods. Secondly, we show that 2D MisMatch outperforms state-of-the-art methods on an MRI-based brain tumour segmentation task. We then further confirm that 3D V-net based MisMatch outperforms its 3D counterpart based on consistency regularisation with input level perturbations, on two different tasks including, left atrium segmentation from 3D CT images and whole brain tumour segmentation from 3D MRI images. Lastly, we find that the performance improvement of MisMatch over the baseline might originate from its better calibration. This also implies that our proposed AI system makes safer decisions than the previous methods.

LDPORR: A localized location privacy protection method based on optimized random response

The broad use of mobile intelligent terminals with locating functions encourages the rapid development of location-based services (LBS), which are widely used in a variety of industries such as social networking, transportation, finance, and entertainment. While enjoying the convenience brought by LBS, the threat of location privacy leakage should not be overlooked. In view of the complex encoding mechanism and low availability of the current location privacy protection methods, a local differential privacy location protection method based on optimized random response is proposed in this paper. A spatial decomposition and Hilbert encoding mechanism are designed based on the Hilbert curve, which can reduce the two-dimensional location data into one-dimensional Hilbert encoding results. A local differential privacy location perturbation method based on optimized random response is proposed to improve the availability of perturbed locations and the accuracy of data aggregation. Experiments on actual location datasets prove that the proposed method can provide better location data availability and operational efficiency based on realizing local differential privacy protection of users’ location.

An optimal management architecture based on digital twin for smart solar-based islands incorporating deep learning and modified particle swarm optimization

Smart island (SI) energy management is a type of energy management system used to ensure that energy is used efficiently on islands. It is designed to reduce energy consumption and costs while also improving the sustainability of the island. Smart island energy management systems use a combination of technologies such as solar, wind, and other renewable energy sources, energy storage systems, smart meters, and advanced analytics to monitor and manage energy usage. The system can be used to provide islanders with real-time energy usage data, allowing them to make informed decisions about their energy use. Using the effects on the environment and economics of the SI as a basis for fully modeling the optimum performance of the SI in grid-connected operations, the feasibility and balance of the SI power grid are ensured. In addition, taking into account the impact of interruptible loads (IL) on SI operation costs, a power shift for IL can be performed using storage batteries in a digital twin environment. A new ecology-driven optimization algorithm has been developed that continuously adjusts migration rates with habitat suitability indexes of normalized individuals and adds a differential perturbation to the migration operator of the migration process. The enhanced particle swarm optimization algorithm has been implemented as the SI optimization dispatching algorithm. In order to have a precise prediction of the renewable energy sources, their output power is predicted using the recurrent neural network (RNN) deep learning model. Based on the simulation outcomes, it is evident that the suggested power dispatching model could greatly decrease the overall price of the system by implementing the advanced and effective algorithm and model presented in the study.

Unraveling the differential perturbations of species-level functional profiling of gut microbiota among phases of methamphetamine-induced conditioned place preference

The gut microbiome plays a significant role in methamphetamine addiction. Previous studies using short-read amplicon sequencing have described alterations in microbiota at the genus level and predicted function, in which taxonomic resolution is insufficient for accurate functional measurements. To address this limitation, we employed metagenome sequencing to intuitively associate species to functions of gut microbiota in methamphetamine-induced conditioned place preference. We observed differential perturbations of species-level functional profiling of the gut microbiota across phases of METH-induced CPP, with alterations in SCFA metabolism and bacterial motility at the acquisition phase and substance dependence-alcoholism pathway and amino acid metabolism at the extinction phase. Our findings suggest that reduced beneficial bacteria, i.e., Lactobacillus reuteri, contributed to the alteration of SCFA metabolism, while the increased abundance of Akkermansia muciniphila during the extinction phase may be associated with altered phenylalanine, tyrosine, and tryptophan metabolism and substance dependence pathway. Our study further supports the association between specific microbial taxa and METH-induced rewarding.

Decreased Expression of Pulmonary Homeobox NKX2.1 and Surfactant Protein C in Developing Lungs That Over-Express Receptors for Advanced Glycation End-Products (RAGE).

Receptors for advanced glycation end-products (RAGE) are multi-ligand cell surface receptors of the immunoglobin superfamily prominently expressed by lung epithelium. Previous experiments demonstrated that over-expression of RAGE by murine alveolar epithelium throughout embryonic development causes neonatal lethality coincident with significant lung hypoplasia. In the current study, we evaluated the expression of NKX2.1 (also referred to as TTF-1), a homeodomain-containing transcription factor critical for branching morphogenesis, in mice that differentially expressed RAGE. We also contextualized NKX2.1 expression with the abundance of FoxA2, a winged double helix DNA binding protein that influences respiratory epithelial cell differentiation and surfactant protein expression. Conditional RAGE over-expression was induced in mouse lung throughout gestation (embryonic day E0-18.5), as well as during the critical saccular period of development (E15.5-18.5), and analyses were conducted at E18.5. Histology revealed markedly less lung parenchyma beginning in the canalicular stage of lung development and continuing throughout the saccular period. We discovered consistently decreased expression of both NKX2.1 and FoxA2 in lungs from transgenic (TG) mice compared to littermate controls. We also observed diminished surfactant protein C in TG mice, suggesting possible hindered differentiation and/or proliferation of alveolar epithelial cells under the genetic control of these two critical transcription factors. These results demonstrate that RAGE must be specifically regulated during lung formation. Perturbation of epithelial cell differentiation culminating in respiratory distress and perinatal lethality may coincide with elevated RAGE expression in the lung parenchyma.

Improving the Sensitivity of Enantioanalysis with Densely Fluorinated NMR Probes.

Nuclear magnetic resonance (NMR) spectroscopy has long been utilized as a classic method for chiral discrimination of enantiomers. However, its sensitivity limitations have hindered the detection of analytes at low concentrations. In this study, we present our efforts to overcome this challenge by employing chiral NMR probes that are labeled with a significant number of chemically equivalent 19F atoms. Specifically, we have designed and synthesized three chiral palladium pincer complexes, all of which are labeled with nonafluoro-tert-butoxy groups to enhance detectability. The recognition of enantiomers with the probe induces distinct changes in microenvironments, resulting in differential perturbations on the chemical shift of the 19F atoms in proximity. This method is applicable to the enantiodifferentiation of various amines, amino alcohols, and amino acid esters. The abundance of 19F atoms enables the detection of chiral analytes at low concentrations, which is otherwise challenging to achieve through traditional 1H NMR-based analysis. Two of the probes are constructed with asymmetric pincer ligands with structurally varied sidearms, allowing for facile manipulation of the chiral binding pocket. The C2 symmetrical probe possesses 36 equivalent 19F atoms, enabling the determination of enantiocomposition of samples with concentrations in the low micromolar range.

Differential Inhibition of Anaplerotic Pyruvate Carboxylation and Glutaminolysis-Fueled Anabolism Underlies Distinct Toxicity of Selenium Agents in Human Lung Cancer.

Past chemopreventive human trials on dietary selenium supplements produced controversial outcomes. They largely employed selenomethionine (SeM)-based diets. SeM was less toxic than selenite or methylseleninic acid (MSeA) to lung cancer cells. We thus investigated the toxic action of these Se agents in two non-small cell lung cancer (NSCLC) cell lines and ex vivo organotypic cultures (OTC) of NSCLC patient lung tissues. Stable isotope-resolved metabolomics (SIRM) using 13C6-glucose and 13C5,15N2-glutamine tracers with gene knockdowns were employed to examine metabolic dysregulations associated with cell type- and treatment-dependent phenotypic changes. Inhibition of key anaplerotic processes, pyruvate carboxylation (PyC) and glutaminolysis were elicited by exposure to MSeA and selenite but not by SeM. They were accompanied by distinct anabolic dysregulation and reflected cell type-dependent changes in proliferation/death/cell cycle arrest. NSCLC OTC showed similar responses of PyC and/or glutaminolysis to the three agents, which correlated with tissue damages. Altogether, we found differential perturbations in anaplerosis-fueled anabolic pathways to underlie the distinct anti-cancer actions of the three Se agents, which could also explain the failure of SeM-based chemoprevention trials.