Assessing nicotine pharmacokinetics of new generation tobacco products and conventional cigarettes: a systematic review and meta-analysis.

INTRODUCTION: New generation tobacco products (NGPs) hold promises as modified-risk alternatives to conventional cigarettes (CCs), given their comparable characteristics. This study investigated the nicotine pharmacokinetics (PK) of NGPs, encompassing closed pod systems, refillable e-cigarettes (ECs), and heated tobacco products (HTPs), in comparison to CCs through systematic review and meta-analysis. METHODS: A comprehensive search was conducted on PubMed, Embase, and Web of Science for articles published between January 2013 and July 2023. Maximum nicotine concentration (Cmax), time to the peak concentration (Tmax), and total nicotine exposure (area under the concentration-time curve, AUC) were extracted to evaluate nicotine delivery PK. Random effects meta-analyses were performed to determine pooled standardized mean differences (SMD), facilitating a comparison of PK profiles between NGPs and CCs. Subgroup analyses exploring flavors and nicotine concentrations across NGPs, and CCs were also conducted. RESULTS: The meta-analysis incorporated 30 articles with 2728 participants. Cmax and AUC were significantly lower for NGPs, while Tmax demonstrated statistical similarity compared to CCs. Among three NGPs, Cmax and AUC were lower for closed pod systems and refillable ECs. In HTPs, Cmax was statistically similar while AUC was lower compared to CCs. Tmax was statistically similar in closed pod systems and HTPs compared to that of CCs. No significant difference was observed in the comparisons of PK between each type of NGPs versus CCs. CONCLUSIONS: NGPs delivered less nicotine than CCs but reached Cmax over a similar timeframe, indicating that NGPs may serve as modified-risk alternatives with lower nicotine delivery to CCs for craving relief and smoking cessation. IMPLICATION: This study suggested that NGPs, such as the closed pod systems, the refillable ECs, and the HTPs, delivered either lower or comparable nicotine levels and achieved peak nicotine concentration at a similar rate as CCs. Our findings carry implications that NGPs can serve as modified-risk nicotine alternative to CCs in helping smokers to manage cravings and potentially quit smoking, thereby highlighting their value in the field of tobacco harm reduction.

Path Planning of Unmanned Aerial Vehicles Based on an Improved Bio-Inspired Tuna Swarm Optimization Algorithm.

The Sine-Levy tuna swarm optimization (SLTSO) algorithm is a novel method based on the sine strategy and Levy flight guidance. It is presented as a solution to the shortcomings of the tuna swarm optimization (TSO) algorithm, which include its tendency to reach local optima and limited capacity to search worldwide. This algorithm updates locations using the Levy flight technique and greedy approach and generates initial solutions using an elite reverse learning process. Additionally, it offers an individual location optimization method called golden sine, which enhances the algorithm's capacity to explore widely and steer clear of local optima. To plan UAV flight paths safely and effectively in complex obstacle environments, the SLTSO algorithm considers constraints such as geographic and airspace obstacles, along with performance metrics like flight environment, flight space, flight distance, angle, altitude, and threat levels. The effectiveness of the algorithm is verified by simulation and the creation of a path planning model. Experimental results show that the SLTSO algorithm displays faster convergence rates, better optimization precision, shorter and smoother paths, and concomitant reduction in energy usage. A drone can now map its route far more effectively thanks to these improvements. Consequently, the proposed SLTSO algorithm demonstrates both efficacy and superiority in UAV route planning applications.

Ultraflexible Ultrathin 3D/1D Hierarchical Interpenetrating Ni-MOF/CNT Buckypaper Composites: Microstructures and Microwave Absorption Properties.

Metal-organic frameworks (MOFs) have attracted attention due to their designable structures. However, recently reported MOF microwave-absorbing materials (MAMs) are dominated by powders. It remains a challenge to design MOF/carbon nanotube (CNT) composite structures that combine the mechanical properties of self-supporting flexibility with excellent microwave absorption. This work involves the hydrothermal approach to grow Ni-MOF of different microstructures in situ on the CNT monofilament by adjusting the molar ratio of nickel ions to organic ligands. Subsequently, an ultraflexible self-supporting Ni-MOF/CNT buckypaper (BP) is obtained by directional gas pressure filtration technology. The BP porous skeleton and the Ni-MOF with a unique porous structure provide effective impedance matching. The CNTs contribute to the conduction loss, the cross-scale heterogeneous interface generated by Ni-MOF/CNT BP provides rich interfacial polarization loss, and the porous structure complicates the microwave propagation path. All factors work together to give Ni-MOF/CNT BP an excellent microwave absorption capacity. The minimum reflection losses of Ni-MOF/CNT BPs decorated with granular-, hollow porous prism-, and porous prism-shaped Ni-MOFs reach -50.8, -57.8, and -43.3 dB, respectively. The corresponding effective absorption bandwidths are 4.5, 6.3, and 4.8 GHz, respectively. Furthermore, BPs show remarkable flexibility as they can be wound hundreds of times around a glass rod with a diameter of 4 mm without structural damage. This work presents a new concept for creating ultraflexible self-supported MOF-based MAMs with hierarchical interpenetrating porous structures, with potential application advantages in the field of flexible electronics.

Repression of PRMT activities sensitize homologous recombination-proficient ovarian and breast cancer cells to PARP inhibitor treatment.

An "induced PARP inhibitor (PARPi) sensitivity by epigenetic modulation" strategy is being evaluated in the clinic to sensitize homologous recombination (HR)-proficient tumors to PARPi treatments. To expand its clinical applications and identify more efficient combinations, we performed a drug screen by combining PARPi with 74 well-characterized epigenetic modulators that target five major classes of epigenetic enzymes. Both type I PRMT inhibitor and PRMT5 inhibitor exhibit high combination and clinical priority scores in our screen. PRMT inhibition significantly enhances PARPi treatment-induced DNA damage in HR-proficient ovarian and breast cancer cells. Mechanistically, PRMTs maintain the expression of genes associated with DNA damage repair and BRCAness and regulate intrinsic innate immune pathways in cancer cells. Analyzing large-scale genomic and functional profiles from TCGA and DepMap further confirms that PRMT1, PRMT4, and PRMT5 are potential therapeutic targets in oncology. Finally, PRMT1 and PRMT5 inhibition act synergistically to enhance PARPi sensitivity. Our studies provide a strong rationale for the clinical application of a combination of PRMT and PARP inhibitors in patients with HR-proficient ovarian or breast cancer.

Multi-Scale Spatio-Temporal Fusion with Adaptive Brain Topology Learning for fMRI Based Neural Decoding.

Neural decoding aims to extract information from neurons' activities to reveal how the brain functions. Due to the inherent spatial and temporal characteristics of brain signals, spatio-temporal computing has become a hot topic for neural decoding. However, the extant spatio-temporal decoding methods usually use static brain topology, ignoring the dynamic patterns of the interaction between brain regions. Further, they do not identify the hierarchical organization of brain topology, leading to only superficial insight into brain spatio-temporal interactions. Therefore, here we propose a novel framework, the Multi-Scale Spatio-Temporal framework with Adaptive Brain Topology Learning (MSST-ABTL), for neural decoding. It includes two new capabilities to enhance spatio-temporal decoding: i) ABTL module, which learns dynamic brain topology while updating specific patterns of brain regions, ii) MSST module, which captures the association of spatial pattern and temporal evolution, and further enhances the interpretability of the learned dynamic topology from multi-scale perspective. We evaluated the framework on the public Human Connectome Project (HCP) dataset (resting-state and task-related fMRI data). The extensive experiments show that the proposed MSST-ABTL outperforms state-of-the-art methods on four evaluation metrics, and also can renew the neuroscientific discoveries in the brain's hierarchical patterns.

Texture Feature Extraction from 1H NMR Spectra for the Geographical Origin Traceability of Chinese Yam.

Adulteration is widespread in the herbal and food industry and seriously restricts traditional Chinese medicine development. Accurate identification of geo-authentic herbs ensures drug safety and effectiveness. In this study, 1H NMR combined intelligent "rotation-invariant uniform local binary pattern" identification was implemented for the geographical origin confirmation of geo-authentic Chinese yam (grown in Jiaozuo, Henan province) from Chinese yams grown in other locations. Our results showed that the texture feature of 1H NMR image extracted with rotation-invariant uniform local binary pattern for identification is far superior compared to the original NMR data. Furthermore, data preprocessing is necessary. Moreover, the model combining a feature extraction algorithm and support vector machine (SVM) classifier demonstrated good robustness. This approach is advantageous, as it is accurate, rapid, simple, and inexpensive. It is also suitable for the geographical origin traceability of other geographical indication agricultural products.

Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization.

Image processing technology has always been a hot and difficult topic in the field of artificial intelligence. With the rise and development of machine learning and deep learning methods, swarm intelligence algorithms have become a hot research direction, and combining image processing technology with swarm intelligence algorithms has become a new and effective improvement method. Swarm intelligence algorithm refers to an intelligent computing method formed by simulating the evolutionary laws, behavior characteristics, and thinking patterns of insects, birds, natural phenomena, and other biological populations. It has efficient and parallel global optimization capabilities and strong optimization performance. In this paper, the ant colony algorithm, particle swarm optimization algorithm, sparrow search algorithm, bat algorithm, thimble colony algorithm, and other swarm intelligent optimization algorithms are deeply studied. The model, features, improvement strategies, and application fields of the algorithm in image processing, such as image segmentation, image matching, image classification, image feature extraction, and image edge detection, are comprehensively reviewed. The theoretical research, improvement strategies, and application research of image processing are comprehensively analyzed and compared. Combined with the current literature, the improvement methods of the above algorithms and the comprehensive improvement and application of image processing technology are analyzed and summarized. The representative algorithms of the swarm intelligence algorithm combined with image segmentation technology are extracted for list analysis and summary. Then, the unified framework, common characteristics, different differences of the swarm intelligence algorithm are summarized, existing problems are raised, and finally, the future trend is projected.

Robust online learning based on siamese network for ship tracking.

The complex and changeable inland river scenes resulting out of frequent occlusions of ships in the available tracking methods are not accurate enough to estimate the motion state of the target ship leading to object tracking drift or even loss. In view of this, an attempt is made to propose a robust online learning ship tracking algorithm based on the Siamese network and the region proposal network. Firstly, the algorithm combines the off-line Siamese network classification score and the online classifier score for discriminative learning, and establishes an occlusion determination mechanism according to the classification the fusion score. When the target is in the occlusion state, the target template is not updated, and the global search mechanism is activated to relocate the target, thereby avoiding object tracking drift. Secondly, an efficient adaptive online update strategy, UpdateNet, is introduced to improve the template degradation in the tracking process. Finally, on comparing the state-of-the-art tracking algorithms on the inland river ship datasets, the experimental results of the proposed algorithm show strong robustness in occlusion scenarios with an accuracy and success rate of 56.8% and 57.2% respectively. Supportive source codes for this research are publicly available at https://github.com/Libra-jing/SiamOL .

Effective detection of Alzheimer's disease by optimizing fuzzy K-nearest neighbors based on salp swarm algorithm.

Alzheimer's disease (AD) is a typical senile degenerative disease that has received increasing attention worldwide. Many artificial intelligence methods have been used in the diagnosis of AD. In this paper, a fuzzy k-nearest neighbor method based on the improved binary salp swarm algorithm (IBSSA-FKNN) is proposed for the early diagnosis of AD, so as to distinguish between patients with mild cognitive impairment (MCI), Alzheimer's disease (AD) and normal controls (NC). First, the performance and feature selection accuracy of the method are validated on 5 different benchmark datasets. Secondly, the paper uses the Structural Magnetic Resolution Imaging (sMRI) dataset, in terms of classification accuracy, sensitivity, specificity, etc., the effectiveness of the method on the AD dataset is verified. The simulation results show that the classification accuracy of this method for AD and MCI, AD and NC, MCI and NC are 95.37%, 100%, and 93.95%, respectively. These accuracies are better than the other five comparison methods. The method proposed in this paper can learn better feature subsets from serial multimodal features, so as to improve the performance of early AD diagnosis. It has a good application prospect and will bring great convenience for clinicians to make better decisions in clinical diagnosis.

Multi-Perspective Feature Extraction and Fusion Based on Deep Latent Space for Diagnosis of Alzheimer's Diseases.

Resting-state functional magnetic resonance imaging (rs-fMRI) has been used to construct functional connectivity (FC) in the brain for the diagnosis and analysis of brain disease. Current studies typically use the Pearson correlation coefficient to construct dynamic FC (dFC) networks, and then use this as a network metric to obtain the necessary features for brain disease diagnosis and analysis. This simple observational approach makes it difficult to extract potential high-level FC features from the representations, and also ignores the rich information on spatial and temporal variability in FC. In this paper, we construct the Latent Space Representation Network (LSRNet) and use two stages to train the network. In the first stage, an autoencoder is used to extract potential high-level features and inner connections in the dFC representations. In the second stage, high-level features are extracted using two perspective feature parses. Long Short-Term Memory (LSTM) networks are used to extract spatial and temporal features from the local perspective. Convolutional neural networks extract global high-level features from the global perspective. Finally, the fusion of spatial and temporal features with global high-level features is used to diagnose brain disease. In this paper, the proposed method is applied to the ANDI rs-fMRI dataset, and the classification accuracy reaches 84.6% for NC/eMCI, 95.1% for NC/AD, 80.6% for eMCI/lMCI, 84.2% for lMCI/AD and 57.3% for NC/eMCI/lMCI/AD. The experimental results show that the method has a good classification performance and provides a new approach to the diagnosis of other brain diseases.

Acute effects of two different work-to-rest ratio of high-intensity interval training on brain-derived neurotrophic factor in untrained young men.

Background: Aerobic exercise could produce a positive effect on the brain by releasing brain-derived neurotrophic factor (BDNF). In untrained healthy humans there seems to be a linear correlation between exercise duration and the positive effect of acute aerobic exercise on brain-derived neurotrophic factor levels. Therefore, we performed two different duration of high-intensity interval training protocols (HIIT), both known to improve cardiovascular fitness, to determine whether then have a similar efficacy in affecting brain-derived neurotrophic factor levels. Methods: 12 untrained young males (aged 23.7 ± 1.8 years), participated in a randomized controlled cross-over trial. They underwent two different work-to-rest ratio high-intensity interval training protocols: high-intensity interval training 1 (30 min, 15 intervals of 1 min efforts at 85%-90% VO2max with 1 min of active recovery at 50%-60% VO2max) and HIIT2 (30 min, 10 intervals of 2 min efforts at 85%-90% VO2max with 1 min of active recovery at 50%-60% VO2max). Serum cortisol, brain-derived neurotrophic factor were collected at baseline, immediately following intervention, and 30 min into recovery for measurements using a Sandwich ELISA method, blood lactate was measured by using a portable lactate analyzer. Results: Our results showed that the similar serum brain-derived neurotrophic factor change in both high-intensity interval training protocols, with maximal serum brain-derived neurotrophic factor levels being reached toward the end of intervention. There was no significant change in serum brain-derived neurotrophic factor from baseline after 30 min recovery. We then showed that both high-intensity interval training protocols significantly increase blood lactate and serum cortisol compared with baseline value (high-intensity interval training p < 0.01; high-intensity interval training 2 p < 0.01), with high-intensity interval training 2 reaching higher blood lactate levels than high-intensity interval training 1 (p = 0.027), but no difference was observed in serum cortisol between both protocols. Moreover, changes in serum brain-derived neurotrophic factor did corelate with change in blood lactate (high-intensity interval training 1 r = 0.577, p < 0.05; high-intensity interval training 2 r = 0.635, p < 0.05), but did not correlate with the change in serum cortisol. Conclusions: brain-derived neurotrophic factor levels in untrained young men are significantly increased in response to different work-to-rest ratio of high-intensity interval training protocols, and the magnitude of increase is exercise duration independent. Moreover, the higher blood lactate did not raise circulating brain-derived neurotrophic factor. Therefore, given that prolonged exercise causes higher levels of cortisol. We suggest that the 1:1work-to-rest ratio of high-intensity interval training protocol might represent a preferred intervention for promoting brain health.

Si-Wu Water Extracts Protect against Colonic Mucus Barrier Damage by Regulating Muc2 Mucin Expression in Mice Fed a High-Fat Diet.

A high-fat diet (HFD) could cause gut barrier damage. The herbs in si-wu (SW) include dang gui (Angelica sinensis (Oliv.) Diels), shu di huang (the processed root of Rehmannia glutinosa Libosch.), chuan xiong (rhizome of Ligusticum chuanxiong Hort.), and bai shao (the root of Paeonia lactiflora f. pilosella (Nakai) Kitag.). Si-wu water extracts (SWE) have been used to treat blood deficiency. Components of one herb from SW have been reported to have anti-inflammatory and anti-obesity activities. However, there have been no reports about the effects of SWE on gut barrier damage. Therefore, the aim of the study was to explore the effect of SWE on gut barrier damage. In this study, we found that SWE effectively controlled body weight, liver weight, and feed efficiency, as well as decreased the serum TC level in HFD-fed mice. Moreover, SWE and rosiglitazone (Ros, positive control) increased the colonic alkaline phosphatase (ALP) level, down-regulated serum pro-inflammatory cytokine levels, and reduced intestinal permeability. In addition, SWE increased goblet cell numbers and mucus layer thickness to strengthen the mucus barrier. After supplementation with SWE and rosiglitazone, the protein expression of CHOP and GRP78 displayed a decrease, which improved the endoplasmic reticulum (ER) stress condition. Meanwhile, the increase in Cosmc and C1GALT1 improved the O-glycosylation process for correct protein folding. These results collectively demonstrated that SWE improved the mucus barrier, focusing on Muc2 mucin expression, in a prolonged high-fat diet, and provides evidence for the potential of SWE in the treatment of intestinal disease-associated mucus barrier damage.

An Energy Efficient and Reliable Multipath Transmission Strategy for Mobile Wireless Sensor Networks.

Multipath data transmission is a key problem that needs to be solved urgently in wireless sensor networks. In this paper, sensor node failure, link failure, energy exhaustion, and external interference affect the stability and reliability of network data transmission. A multipath transmission strategy for wireless sensor networks based on improved shuffled frog leaping algorithm is proposed. A mathematical model of multipath transmission in wireless sensor networks is established. In the shuffled frog leaping algorithm, combined with the transition probability in the particle swarm optimization algorithm, random individuals in the subgroup are introduced to assist the search when updating the frog individual position, which improves the algorithm's ability to jump out of the local optimum and improves the quality of the optimization algorithm solution. The model is applied to multipath transmission in wireless sensor networks. Then, the shuffled frog leaping algorithm is used to update, divide, and reorganize the sensor nodes to select the optimal node to establish the optimal transmission path and improve the stability and reliability of the network. Simulation experiments show that the algorithm in this paper can ensure the reliability of data transmission, reduce the network packet loss rate and network energy consumption, and reduce the average delay of data transmission.

A Data Collection Method for Mobile Wireless Sensor Networks Based on Improved Dragonfly Algorithm.

For the sensing layer of the Internet of Things, the mobile wireless sensor network has problems such as limited energy of the sensor nodes, unbalanced energy consumption, unreliability, and long transmission delay in the data collection process. It is proved by mathematical derivation and theory that this is a typical multiobjective optimization problem. In this paper, the optimization goal is to minimize the energy consumption and improve the reliability under time-delay constraints and propose a path optimization mechanism to optimize the mobile Sink of mobile wireless sensor networks based on the improved dragonfly optimization algorithm. The algorithm takes full advantage of the abundant storage space, sufficient energy, and strong computing power of the mobile Sink to ensure network connectivity and improve network communication efficiency. Through simulation comparison and analysis, compared with random movement method, artificial bee colony algorithm, and basic dragonfly optimization algorithm, the energy consumption of the network is reduced, the lifespan of the network is increased, and the connectivity and transmission delay of the network are improved. The proposed algorithm balances the energy consumption of the sensors nodes to meet the network service quality and improve the reliability of the network.

Lupus nephritis diagnosis using enhanced moth flame algorithm with support vector machines.

Systemic lupus erythematosus is a chronic autoimmune disease that affects the kidney in most patients. Lupus nephritis (LN) is divided into six categories by the International Society of Nephrology/Renal Pathology Society (ISN/RPS). The purpose of this research is to build a framework for discriminating between ISN/RPS pure class V(MLN) and classes III ± V or IV ± V (PLN) using real clinical data. The framework is developed by merging a hybrid stochastic optimizer, moth-flame algorithm (HMFO), with a support vector machine (SVM), dubbed HMFO-SVM. The HMFO is constructed by enhancing the original moth-flame algorithm (MFO) with a bee-foraging learning operator, which guarantees that the algorithm speeds convergence and departs from the local optimum. The HMFO is used to optimize parameters and select features simultaneously for SVM on clinical SLE data. On 23 benchmark tests, the suggested HMFO method is validated. Finally, clinical data from LN patients are analyzed to determine the efficacy of HMFO-SVM over other SVM rivals. The statistical findings indicate that all measures have predictive capabilities and that the suggested HMFO-SVM is more stable for analyzing systemic LN. HMFO-SVM may be used to analyze LN as a feasible computer-assisted technique.

Hierarchical Harris hawks optimization for epileptic seizure classification.

The intelligent recognition of electroencephalogram (EEG) signals is a valuable tool for epileptic seizure classification. Given that visual inspection of EEG signals is time-consuming, and that mutant signals dramatically increase the workload of neurologists, automatic epilepsy diagnosis systems are extremely helpful. However, the existing epilepsy diagnosis methods suffer from some shortcomings. For example, they tend to fall into local optima quickly because of their failure to fully consider the discriminative features of EEG signals. To tackle this problem, in this article, an enhanced automatic epilepsy diagnosis method is proposed using time-frequency analysis and improved Harris hawks optimization (IHHO) with a hierarchical mechanism. Specifically, the signal is decomposed into five rhythms using continuous wavelet transform, with the local and global features extracted using the local binary pattern and the gray level co-occurrence matrix. Discriminative features are then selected and further mapped to the final recognition results using both IHHO and the k-nearest neighbor classifier. To evaluate its performance, the proposed method was compared with a variety of classical meta-heuristic algorithms on 23 benchmark functions. Moreover, the proposed approach achieved more than 99.67% accuracy on the Bonn dataset and 99.06% accuracy on the CHB-MIT dataset, out-performing a multitude of state-of-the-art methods. Taken together, these results demonstrate the utility of our approach in the automatic diagnosis of epilepsy. Supportive datasets and source codes for this research are publicly available at https://github.com/sstudying/lzzhen, and latest updates for the HHO algorithm are provided at https://aliasgharheidari.com/HHO.html.

The effect of information literacy heterogeneity on epidemic spreading in information and epidemic coupled multiplex networks.

With the COVID-19 pandemic, better understanding of the co-evolution of information and epidemic diffusion networks is important for pandemic-related policies. Using the microscopic Markov chain method, this study proposed an aware-susceptible-infected model (ASI) to explore the effect of information literacy on the spreading process in such multiplex networks. We first introduced a parameter that adjusts the self-protection related execution ability of aware individuals in order to emphasis the importance of protective behaviors compared to awareness in decreasing the infection probability. The model also captures individuals' heterogeneity in their information literacy. Simulation experiments found that the high information-literate individuals are more sensitive to information adoption. In addition, epidemic information can help to suppress the epidemic diffusion only when individuals' abilities of transforming awareness into actual protective behaviors attain a threshold. In communities dominated by highly literate individuals, a larger information literacy gap can improve awareness acquisition and thus help to suppress the epidemic among the whole group. By contrast, in communities dominated by low information-literate individuals, a smaller information literacy gap can better prevent the epidemic diffusion. This study contributes to the literature by revealing the importance of individuals' heterogeneity of information literacy on epidemic spreading in different communities and has implications for how to inform people when a new epidemic disease emerges.

Characterization of trichome-specific BAHD acyltransferases involved in acylsugar biosynthesis in Nicotiana tabacum.

Glandular trichomes of tobacco (Nicotiana tabacum) produce blends of acylsucroses that contribute to defence against pathogens and herbivorous insects, but the mechanism of assembly of these acylsugars has not yet been determined. In this study, we isolated and characterized two trichome-specific acylsugar acyltransferases that are localized in the endoplasmic reticulum, NtASAT1 and NtASAT2. They sequentially catalyse two additive steps of acyl donors to sucrose to produce di-acylsucrose. Knocking out of NtASAT1 or NtASAT2 resulted in deficiency of acylsucrose; however, there was no effect on acylsugar accumulation in plants overexpressing NtASAT1 or NtASAT2. Genomic analysis and profiling revealed that NtASATs originated from the T subgenome, which is derived from the acylsugar-producing diploid ancestor N. tomentosiformis. Our identification of NtASAT1 and NtASAT2 as enzymes involved in acylsugar assembly in tobacco potentially provides a new approach and target genes for improving crop resistance against pathogens and insects.

Systematic illumination of druggable genes in cancer genomes.

By combining 6 druggable genome resources, we identify 6,083 genes as potential druggable genes (PDGs). We characterize their expression, recurrent genomic alterations, cancer dependencies, and therapeutic potentials by integrating genome, functionome, and druggome profiles across cancers. 81.5% of PDGs are reliably expressed in major adult cancers, 46.9% show selective expression patterns, and 39.1% exhibit at least one recurrent genomic alteration. We annotate a total of 784 PDGs as dependent genes for cancer cell growth. We further quantify 16 cancer-related features and estimate a PDG cancer drug target score (PCDT score). PDGs with higher PCDT scores are significantly enriched for genes encoding kinases and histone modification enzymes. Importantly, we find that a considerable portion of high PCDT score PDGs are understudied genes, providing unexplored opportunities for drug development in oncology. By integrating the druggable genome and the cancer genome, our study thus generates a comprehensive blueprint of potential druggable genes across cancers.

Body fat prediction through feature extraction based on anthropometric and laboratory measurements.

Obesity, associated with having excess body fat, is a critical public health problem that can cause serious diseases. Although a range of techniques for body fat estimation have been developed to assess obesity, these typically involve high-cost tests requiring special equipment. Thus, the accurate prediction of body fat percentage based on easily accessed body measurements is important for assessing obesity and its related diseases. By considering the characteristics of different features (e.g. body measurements), this study investigates the effectiveness of feature extraction for body fat prediction. It evaluates the performance of three feature extraction approaches by comparing four well-known prediction models. Experimental results based on two real-world body fat datasets show that the prediction models perform better on incorporating feature extraction for body fat prediction, in terms of the mean absolute error, standard deviation, root mean square error and robustness. These results confirm that feature extraction is an effective pre-processing step for predicting body fat. In addition, statistical analysis confirms that feature extraction significantly improves the performance of prediction methods. Moreover, the increase in the number of extracted features results in further, albeit slight, improvements to the prediction models. The findings of this study provide a baseline for future research in related areas.