Precise control of microtubule disassembly in living cells.

Microtubules tightly regulate various cellular activities. Our understanding of microtubules is largely based on experiments using microtubule-targeting agents, which, however, are insufficient to dissect the dynamic mechanisms of specific microtubule populations, due to their slow effects on the entire pool of microtubules. To overcome this technological limitation, we have used chemo and optogenetics to disassemble specific microtubule subtypes, including tyrosinated microtubules, primary cilia, mitotic spindles, and intercellular bridges, by rapidly recruiting engineered microtubule-cleaving enzymes onto target microtubules in a reversible manner. Using this approach, we show that acute microtubule disassembly swiftly halts vesicular trafficking and lysosomal dynamics. It also immediately triggers Golgi and ER reorganization and slows the fusion/fission of mitochondria without affecting mitochondrial membrane potential. In addition, cell rigidity is increased after microtubule disruption owing to increased contractile stress fibers. Microtubule disruption furthermore prevents cell division, but does not cause cell death during interphase. Overall, the reported tools facilitate detailed analysis of how microtubules precisely regulate cellular architecture and functions.

RTL1/PEG11 imprinted in human and mouse brain mediates anxiety-like and social behaviors and regulates neuronal excitability in the locus coeruleus.

RTL1/PEG11, which has been associated with anxiety disorders, is a retrotransposon-derived imprinted gene in the placenta. However, imprinting patterns and functions of RTL1 in the brain have not been well-investigated. We found Rtl1 was paternally, but not maternally, expressed in brain stem, thalamus, and hypothalamus of mice, and imprinting status of RTL1 was maintained in human brain. Paternal Rtl1 knockout (Rtl1m+/p-) mice had higher neonatal death rates due to impaired suckling, and low body weights beginning on embryonic day 16.5. High paternal expression of Rtl1 was detected in the locus coeruleus (LC) and Rtl1m+/p- mice showed an increased delay in time of onset for action potentials and inward currents with decreased neuronal excitability of LC neurons. Importantly, Rtl1m+/p- mice exhibited behaviors associated with anxiety, depression, fear-related learning and memory, social dominance, and low locomotor activity. Taken together, our findings demonstrate RTL1 is imprinted in brain, mediates emotional and social behaviors, and regulates excitability in LC neurons.

Neuronal NLRP3 inflammasome mediates spreading depolarization-evoked trigeminovascular activation.

Spreading depolarization (SD), the underlying mechanism of migraine aura, may trigger the opening of the pannexin 1 (PANX1) pore to sustain the cortical neuroinflammatory cascades involved in the genesis of headache. Yet, the mechanism underlying SD-evoked neuroinflammation and trigeminovascular activation remains incompletely understood. We characterized the identity of inflammasome activated following SD-evoked PANX1 opening. Pharmacological inhibitors targeting PANX1 or NLRP3 as well as genetic ablation of Nlrp3 and Il1b were applied to investigate the molecular mechanism of the downstream neuroinflammatory cascades. In addition, we examined whether SD-triggered microglial activation facilitates neuronal NLRP3-mediated inflammatory cascades. Pharmacological inhibition of toll-like receptors TLR2/4, the potential receptors of the damage-associated molecular pattern HMGB1, was further employed to interrogate the neuron-microglia interplay in SD-induced neuroinflammation. We found that NLRP3 but not NLRP1 or NLRP2 inflammasome was activated following PANX1 opening after single or multiple SDs evoked by either KCl topical application or non-invasively with optogenetics. The SD-evoked NLRP3 inflammasome activation was observed exclusively in neurons but not microglia or astrocytes. Proximity ligation assay demonstrated that the assembly of the NLRP3 inflammasome occurred as early as 15 min after SD. Genetic ablation of Nlrp3 or Il1b or pharmacological inhibition of PANX1 or NLRP3 ameliorated SD-induced neuronal inflammation, middle meningeal artery dilatation, calcitonin gene-related peptide expression in trigeminal ganglion and c-Fos expression in trigeminal nucleus caudalis. Moreover, multiple SDs induced microglial activation subsequent to neuronal NLRP3 inflammasome activation, which in turn orchestrated with neurons to mediate cortical neuroinflammation, as demonstrated by decreased neuronal inflammation after pharmacological inhibition of microglia activation or blockade of the TLR2/4 receptors. To conclude, single or multiple SDs evoked activation of neuronal NLRP3 inflammasomes and its downstream inflammatory cascades to mediate cortical neuroinflammation and trigeminovascular activation. In the context of multiple SDs, the cortical inflammatory processes could be facilitated by SD-evoked microglia activation. These findings may implicate the potential role of innate immunity in migraine pathogenesis.

The effect of a bone-preserving board game program on the knowledge, attitudes and preventive behaviors of osteoporosis in older adults.

Osteoporosis predisposes to fractures, which affect the quality of life and can be life-threatening. However, the knowledge, attitudes and preventive behaviors of osteoporosis in older adults are insufficient. The aim of this paper was to develop and test the effect of a bone-preserving board game program among older adults. A convenience sample of 85 older adults recruited from two community activity centers in southern Taiwan were assigned to either an experimental group or a control group. The experimental group played a bone-preserving board game for 4 weeks, and the control group participated in routine community center activities. The generalized estimating equation showed significantly larger improvements in knowledge, attitudes, and preventive behaviors in the experimental group than in the control group. Board games designed for older adults can support public health education and help prevent osteoporosis. Our results provide a reference for educators, clinical practitioners and researchers.

Investigation of New Psychoactive Substances (NPS), Other Illicit Drugs, and Drug-Related Compounds in a Taiwanese Wastewater Sample Using High-Resolution Mass-Spectrometry-Based Targeted and Suspect Screening.

The proliferation of new psychoactive substances (NPSs) in recent years has posed a significant challenge to public health. Traditional monitoring methods have proven insufficient in tracking these constantly evolving substances, leading to the development of alternative approaches such as wastewater-based epidemiology (WBE). The present study aims to utilize high-resolution mass spectrometry (HRMS)-based targeted and suspect screening to profile NPS, other illicit drugs, and drug-related compounds in a Taiwanese wastewater sample. For the targeted analysis, 8 out 18 standards of illicit drugs have been identified. The suspect screening approach based on approximately 3600 substances in the SWGDRUG library can further identify 92 compounds, including opiate analgesics, synthetic cathinones, phenylalkylamines derivatives, phenethylamine derivatives, tryptamine derivatives, steroids, and ephedrine-related compounds. Additionally, the presence of 5-methoxy-2-aminoindane (MEAI) in the wastewater indicates that drug dealers have recently sold this potential NPS to evade drug regulations. This study firstly reports the HRMS-based comprehensive profile of NPS, other illicit drugs, and drug-related compounds in Taiwan, which could be applied as biomarkers for estimating the consumption of drugs.

The efficacy of a mindfulness-based exercise program in older residents of a long-term care facility in Taiwan.

Older people living in long-term care facilities remain largely inactive, and therefore promoting exercise in this population is necessary. This study evaluated the efficacy of a mindfulness-based exercise program in older residents of a long-term care facility in Taiwan. A convenience sample of 72 older residents of a long-term care facility were recruited and assigned to either an experimental group or a control group. The experimental group (n = 36) participated in an 8-week mindfulness-based exercise program, and the control group (n = 36) received routine care. The generalized estimating equation showed significantly larger improvements in a fear of falling, exercise self-efficacy, dynamic balance, and muscle strength in the experimental group than in the control group from baseline to the end of the intervention and 3 months after the end of the intervention. This study provides a reference for how to improve exercise practice in older people living in long-term care facilities.

Fluid intelligence is associated with cortical volume and white matter tract integrity within multiple-demand system across adult lifespan.

BACKGROUND: Fluid intelligence (Gf) is the innate ability of an individual to respond to complex and unexpected situations. Although some studies have considered that the multiple-demand (MD) system of the brain was the biological foundation for Gf, further characterization of their relationships in the context of aging is limited. The present study hypothesized that the structural metrics of the MD system, including cortical thickness, cortical volumes, and white matter (WM) tract integrity, was the brain correlates for Gf across the adult life span. Partial correlation analysis was performed to investigate whether the MD system could still explain Gf independent of the age effect. Moreover, the partial correlations between Gf and left/right structural metrics within the MD regions were compared to test whether the correlations displayed distinct lateralization. METHODS: The participants were recruited from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) databank, comprising the images of 603 healthy participants aged 18-88 years acquired on a 3-T system. The MRI data included high-resolution T1-weighted and diffusion-weighted images, from which gray matter and WM structural metrics of the MD system were analyzed, respectively. The structural metrics of gray matter were quantified in terms of cortical volume/thickness of five pairs of cortical regions, and those of WM were quantified in terms of the mean axial diffusivity (DA), radial diffusivity (DR), mean diffusivity (DM), and generalized fractional anisotropy (GFA) on five pairs of tracts. Partial correlation controlling for age and sex effects, was performed to investigate the associations of Gf scores with the mean DA, DR, DM and GFA of all tracts in the MD system, those of left and right hemispheric tracts, and those of each tract. Fisher's exact test was used to compare the partial correlations between left and right MD regions. RESULTS: The linear relationship between cortical volumes and Gf was evident across all levels of the MD system even after controlling for age and sex. For the WM integrity, diffusion indices including DA, DR, DM and GFA displayed linear relationships with Gf scores at various levels of the MD system. Among the 10 WM tracts connecting the MD regions, bilateral superior longitudinal fasciculus I and bilateral frontal aslant tracts exhibited the strongest and significant associations. Our results did not show significant inter-hemispheric differences in the associations between structural metrics of the MD system and Gf. CONCLUSION: Our results demonstrate significant associations between Gf and both cortical volumes and tract integrity of the MD system across the adult lifespan in a population-based cohort. We found that the association remained significant in the entire adult lifespan despite simultaneous decline of Gf and the MD system. Our results suggest that the MD system might be a structural underpinning of Gf and support the fronto-parietal model of cognitive aging. However, we did not find hemispheric differences in the Gf-MD correlations, not supporting the hemi-aging hypothesis.

Diminution of context association memory structure in subjects with subjective cognitive decline.

Alzheimer's disease (AD) progresses insidiously from the preclinical stage to dementia. While people with subjective cognitive decline (SCD) have normal cognitive performance, some may be in the preclinical stage of AD. Neurofibrillary tangles appear first in the transentorhinal cortex, followed by the entorhinal cortex in the clinically silent stage of AD. We expected the earliest changes in subjects with SCD to occur in medial temporal subfields other than the hippocampal proper. These selective structural changes would affect specific memory subcomponents. We used the Family Picture subtest of the Wechsler Memory Scale-III, which was modified to separately compute character, activity, and location subscores for episodic memory subcomponents. We recruited 43 subjects with SCD, 44 subjects with amnesic mild cognitive impairment, and 34 normal controls. MRI was used to assess cortical thickness, subcortical gray matter volume, and fractional anisotropy. The results demonstrated that SCD subjects showed significant cortical atrophy in their bilateral parahippocampus and perirhinal and the left entorhinal cortices but not in their hippocampal regions. SCD subjects also exhibited significantly decreased mean fractional anisotropy in their bilateral uncinate fasciculi. The diminution of cortical thickness over the mesial temporal subfields corresponded to brain areas with early tangle deposition, and early degradation of the uncinate fasciculus was in accordance with the retrogenesis hypothesis. The parahippocampus and perirhinal cortex contribute mainly to context association memory while the entorhinal cortex, along with the uncinate fasciculus, contributes to content-related contextual memory. We proposed that context association and related memory structures are vulnerable in the SCD stage.

CURE: A deep learning framework pre-trained on large-scale patient data for treatment effect estimation.

Treatment effect estimation (TEE) aims to identify the causal effects of treatments on important outcomes. Current machine-learning-based methods, mainly trained on labeled data for specific treatments or outcomes, can be sub-optimal with limited labeled data. In this article, we propose a new pre-training and fine-tuning framework, CURE (causal treatment effect estimation), for TEE from observational data. CURE is pre-trained on large-scale unlabeled patient data to learn representative contextual patient representations and fine-tuned on labeled patient data for TEE. We present a new sequence encoding approach for longitudinal patient data embedding both structure and time. Evaluated on four downstream TEE tasks, CURE outperforms the state-of-the-art methods, marking a 7% increase in area under the precision-recall curve and an 8% rise in the influence-function-based precision of estimating heterogeneous effects. Validation with four randomized clinical trials confirms its efficacy in producing trial conclusions, highlighting CURE's capacity to supplement traditional clinical trials.

Optimal positions of clear aligner attachments to achieve lower canine tipping and bodily movement using finite element analysis.

Background/purpose: Clear aligners are popular orthodontic tools because of their relatively aesthetic appearance and convenience of use. Nevertheless, bodily tooth movements still present challenges. This study evaluated various configurations of attachments placed on the mandibular canine in terms of the efficiency of canine bodily movement and correction of tipping. Materials and methods: A finite element model of the mandible was constructed to investigate the effects of various attachment configurations on the overall bodily movement and undesirable tipping of a mandibular canine. Canine movements were categorized into four types, namely tipping and bodily movements in the mesial and distal directions. The size and shape of the attachments were fixed, but their placement and orientation were varied. Five and seven attachment configurations were evaluated for their influence on tipping and bodily movements, respectively. Results: Attachment configuration significantly influenced mandibular canine tipping. The mesial occlusal-distal cervical and mesial occlusal-mesial cervical configurations had notable effects on mesial tipping, and the mesial occlusal-mesial cervical configuration excelled in distal tipping by increasing strain by 33.1%. The mesial occlusal-mesial cervical attachment configuration consistently had superior efficiency in facilitating both mesial and distal bodily movements of the canine. Conclusion: The mesial occlusal-mesial cervical attachment configuration excelled in all four types of canine movement. Irrespective of the attachment configuration, canines tend to move overall with slight tipping due to skeletal resistance and their center of rotation. The attachment configuration is crucial to the success of clear aligner treatment and must be carefully considered in clinical practice.

SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing.

Sepsis is the leading cause of in-hospital mortality in the USA. Early sepsis onset prediction and diagnosis could significantly improve the survival of sepsis patients. Existing predictive models are usually trained on high-quality data with few missing information, while missing values widely exist in real-world clinical scenarios (especially in the first hours of admissions to the hospital), which causes a significant decrease in accuracy and an increase in uncertainty for the predictive models. The common method to handle missing values is imputation, which replaces the unavailable variables with estimates from the observed data. The uncertainty of imputation results can be propagated to the sepsis prediction outputs, which have not been studied in existing works on either sepsis prediction or uncertainty quantification. In this study, we first define such propagated uncertainty as the variance of prediction output and then introduce uncertainty propagation methods to quantify the propagated uncertainty. Moreover, for the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm to increase confidence by actively recommending clinicians to observe the most informative variables. We validate the proposed models in both publicly available data (i.e., MIMIC-III and AmsterdamUMCdb) and proprietary data in The Ohio State University Wexner Medical Center (OSUWMC). The experimental results show that the propagated uncertainty is dominant at the beginning of admissions to hospitals and the proposed algorithm outperforms state-of-the-art active sensing methods. Finally, we implement a SepsisLab system for early sepsis prediction and active sensing based on our pre-trained models. Clinicians and potential sepsis patients can benefit from the system in early prediction and diagnosis of sepsis.

Clinicopathological features and cancer transcriptomic profiling of poorly cohesive gastric carcinoma subtypes.

Gastric poorly cohesive carcinoma (PCC) manifests with a diffuse pattern and diverse tumor cell morphologies, often indicating a more unfavorable prognosis. Recent consensus has reclassified PCC based on the proportion of signet-ring cells (SRCs) in tumors for research purposes. The two most distinct subtypes, poorly cohesive carcinoma not otherwise specified (PCC-NOS) and signet-ring cell carcinoma (SRCC), are characterized by less than 10% and more than 90% SRCs, respectively. However, research comparing the clinicopathological and transcriptomic differences between these subtypes remains limited. In this study, we conducted a comparative analysis of clinicopathological features in 55 advanced-stage PCCs, consisting of 43 PCC-NOS and 12 SRCC cases. Subsequently, 12 PCC-NOS and 5 SRCC cases were randomly selected for initial cancer-related gene expression profiling and pathway enrichment analysis using the GeoMx digital spatial profiler, followed by validation in a separate validation group comprising 16 PCC-NOS and 6 SRCC cases. These transcriptomic findings were then correlated with tumor morphology and clinicopathological data. PCC-NOS cases exhibited larger tumor size, a higher prevalence of pathological N3 disease, and a worse 1-year progression-free survival rate compared to SRCC cases. Clustering of PCC-NOS and SRCC was successfully achieved using the GeoMx Cancer Transcriptome Atlas. Among all studied genes, only MMP7 showed differential expression, with its overexpression significantly associated with the PCC-NOS subtype, increased perineural invasion, and earlier disease progression. Pathway analysis revealed significantly enriched pathways in PCC-NOS related to vesicle-mediated transport, adaptive immune systems, oncogenic signaling, and extracellular matrix organization, while SRCC displayed significant enrichment in pathways associated with respiratory electron transport and the cell cycle. In conclusion, this study compares and correlates clinicopathological features and transcriptomic data between PCC-NOS and SRCC at advanced stages, employing the latest consensus classification and a novel platform for analysis.

Network properties determine neural network performance.

Machine learning influences numerous aspects of modern society, empowers new technologies, from Alphago to ChatGPT, and increasingly materializes in consumer products such as smartphones and self-driving cars. Despite the vital role and broad applications of artificial neural networks, we lack systematic approaches, such as network science, to understand their underlying mechanism. The difficulty is rooted in many possible model configurations, each with different hyper-parameters and weighted architectures determined by noisy data. We bridge the gap by developing a mathematical framework that maps the neural network's performance to the network characters of the line graph governed by the edge dynamics of stochastic gradient descent differential equations. This framework enables us to derive a neural capacitance metric to universally capture a model's generalization capability on a downstream task and predict model performance using only early training results. The numerical results on 17 pre-trained ImageNet models across five benchmark datasets and one NAS benchmark indicate that our neural capacitance metric is a powerful indicator for model selection based only on early training results and is more efficient than state-of-the-art methods.

The role of genetic testing in adult patients with unexplained epilepsy.

OBJECTIVE: Genetic causes are often overlooked in patients with epilepsy of unknown etiology, particularly in adults. We aimed to evaluate clinical features of genetic epilepsy and the utility of genetic testing. METHODS: We retrospectively screened consecutive unrelated adult epilepsy patients at an epilepsy clinic from April 2022 to May 2023. Patients with unknown etiology or special brain lesions were classified as unexplained epilepsy. In them, patients with young-onset seizures or family history of seizures who were recommended for and ultimately underwent genetic testing using either panel next-generation sequencing (NGS) or whole-exome sequencing (WES) were enrolled. A definite or probable genetic diagnosis was established through genotype-phenotype correlation. We compared the demographic characteristics between genetic epilepsy and other etiologies. RESULTS: Of the 374 adult epilepsy patients, 258 were classified as unexplained epilepsy, 129 were suspected of having genetic epilepsy due to young-onset seizures or a positive family history, 33 underwent genetic testing; 13 harbored variants classified as pathogenic, and 6 reached a definite genetic diagnosis, resulting in a yield of 18%. Among the 27 patients without a definite genetic diagnosis, 7 had a nongenetic structural etiology. Patients with genetic etiology exhibited greater multisystem involvement particularly multiple structural anomalies and early childhood-onset seizures, but wasn't directly correlated with young-onset seizures or a positive family history. The diagnostic yield was comparable between panel NGS and WES. SIGNIFICANCE: In adult patients with unexplained epilepsy, genetic epilepsy is more associated with multisystem involvement and multiple structural anomalies but not family history of seizures or young-onset seizures.

The flavonoid hydroxygenkwanin reduces inflammation and neointimal formation.

Abnormal vascular smooth muscle cell (VSMC) proliferation and migration play crucial roles in neointimal hyperplasia and restenosis progression in response to stimulation with various inflammatory cytokines, such as platelet-derived growth factor-BB (PDGF-BB) and tumour necrosis factor-α (TNF-α). Hydroxygenkwanin (HGK) exerts remarkable anti-inflammatory, antitumour, antiproliferative and antimigratory effects. The aim of the study was to elucidate the therapeutic effect and regulatory mechanism of HGK on neointimal hyperplasia. The results showed that HGK inhibited the abnormal proliferation, migration, and inflammation of PDGF-BB- or TNF-α-treated VSMCs through regulation of the PDK1/AKT/mTOR pathway. In addition, HGK promoted circulating endothelial progenitor cell (EPC) chemotaxis. In an in vivo assay, HGK dramatically enhanced re-endothelization and reduced neointimal hyperplasia after femoral artery denudation with a guide wire in mice. These results suggest that HGK can serve as a therapeutic target drug or a functional food supplement for the treatment of restenosis.

Study on Anti-Inflammatory Effects of and Muscle Recovery Associated with Transdermal Delivery of Chaenomeles speciosa Extracts Using Supersonic Atomizer on Rat Model.

Repetitive motion or exercise is associated with oxidative stress and muscle inflammation, which can lead to declining grip strength and muscle damage. Oleanolic acid and ursolic acid have anti-inflammatory and antioxidant properties and can be extracted from Chaenomeles speciosa through ultrasonic sonication. We investigated the association between grip strength declines and muscle damage induced by lambda carrageenan (LC) injection and exercise exposure in rats. We also assessed the reparative effects of transdermal pretreatment and post-treatment with C. speciosa extracts (CSEs) by using a supersonic atomizer. The half-maximal inhibitory concentration (IC50) of CSEs for cells was 10.5 mg/mL. CSEs significantly reduced the generation of reactive oxygen species and inflammatory factors (interleukin [IL]-6 and IL-1ß) in in vitro cell tests. Rats subjected to LC injection and 6 weeks of exercise exhibited significantly increased inflammatory cytokine levels (IL-1ß, TNF-α, and IL-6). Hematoxylin and eosin staining revealed inflammatory cell infiltration and evident muscle damage in the gastrocnemius muscle, which exhibited splitting and the appearance of the endomysium and perimysium. The treated rats' grip strength significantly declined. Following treatment with CSEs, the damaged muscles exhibited decreased IL-1ß, TNF-α, and IL-6 levels and normal morphologies. Moreover, grip strength significantly recovered. Pretreatment with CSEs yielded an immediate and significant increase in grip strength, with an increase of 180% and 165% occurring in the rats exposed to LC injection and exercise within the initial 12 h period, respectively, compared with the control group. Pretreatment with CSEs delivered transdermally using a supersonic atomizer may have applications in sports medicine and training or competitions.

Rationally designed febuxostat-based hydroxamic acid and its pH-Responsive nanoformulation elicits anti-tumor activity.

Attempts to furnish antitumor structural templates that can prevent the occurrence of drug-induced hyperuricemia spurred us to generate xanthine oxidase inhibitor-based hydroxamic acids and anilides. Specifically, the design strategy involved the insertion of febuxostat (xanthine oxidase inhibitor) as a surface recognition part of the HDAC inhibitor pharmacophore model. Investigation outcomes revealed that hydroxamic acid 4 elicited remarkable antileukemic effects mediated via HDAC isoform inhibition. Delightfully, the adduct retained xanthine oxidase inhibitory activity, though xanthine oxidase inhibition was not the underlying mechanism of its cell growth inhibitory effects. Also, compound 4 demonstrated significant in-vivo anti-hyperuricemic (PO-induced hyperuricemia model) and antitumor activity in an HL-60 xenograft mice model. Compound 4 was conjugated with poly (ethylene glycol) poly(aspartic acid) block copolymer to furnish pH-responsive nanoparticles (NPs) in pursuit of circumventing its cytotoxicity towards the normal cell lines. SEM analysis revealed that NPs had uniform size distributions, while TEM analysis ascertained the spherical shape of NPs, indicating their ability to undergo self-assembly. HDAC inhibitor 4 was liberated from the matrix due to the polymeric nanoformulation's pH-responsiveness, and the NPs demonstrated selective cancer cell targeting ability.

A bovine whey protein extract can induce the generation of regulatory T cells and shows potential to alleviate asthma symptoms in a murine asthma model.

The number of people with asthma has dramatically increased over the past few decades and the cost of care is more than $11·3 billion per year. The use of steroids is the major treatment to control asthma symptoms, but the side effects are often devastating. Seeking new drugs or new strategies to reduce the dose of steroid taken has always been an important task. A bovine whey protein extract (WPE), which is enriched in transforming growth factor-ß (TGF-ß), has been demonstrated to have the potential for reducing symptoms associated with mild-to-moderate T-helper cell type 1-mediated psoriasis in human subjects. However, whether WPE also has potential for inhibiting T-helper cell type 2 (Th2)-mediated disease remains unclear. In the present study, using a murine asthma model, we found that sensitised mice fed WPE daily, before they were challenged, resulted in reducing airway inflammation, serum ovalbumin-specific IgE, Th2-related cytokine production and airway hyperresponsiveness. Increase in the regulatory T cell (Treg) population in vitro and in vivo was observed when treated with WPE. According to the results from the TGF-ß-blocking antibody study, we suggest that TGF-ß is the main component that endows WPE with the potential to reduce the generation of Treg. Thus, the present data suggest that WPE has the potential to alleviate the symptoms of asthma by inducing the generation of Treg. Therefore, regular administration of WPE might be potentially beneficial for patients with asthma.

Benchmarking machine learning robustness in Covid-19 genome sequence classification.

The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 genome-millions of sequences and counting. This amount of data, while being orders of magnitude beyond the capacity of traditional approaches to understanding the diversity, dynamics, and evolution of viruses, is nonetheless a rich resource for machine learning (ML) approaches as alternatives for extracting such important information from these data. It is of hence utmost importance to design a framework for testing and benchmarking the robustness of these ML models. This paper makes the first effort (to our knowledge) to benchmark the robustness of ML models by simulating biological sequences with errors. In this paper, we introduce several ways to perturb SARS-CoV-2 genome sequences to mimic the error profiles of common sequencing platforms such as Illumina and PacBio. We show from experiments on a wide array of ML models that some simulation-based approaches with different perturbation budgets are more robust (and accurate) than others for specific embedding methods to certain noise simulations on the input sequences. Our benchmarking framework may assist researchers in properly assessing different ML models and help them understand the behavior of the SARS-CoV-2 virus or avoid possible future pandemics.

Assessing the Resilience of Machine Learning Classification Algorithms on SARS-CoV-2 Genome Sequences Generated with Long-Read Specific Errors.

The emergence of third-generation single-molecule sequencing (TGS) technology has revolutionized the generation of long reads, which are essential for genome assembly and have been widely employed in sequencing the SARS-CoV-2 virus during the COVID-19 pandemic. Although long-read sequencing has been crucial in understanding the evolution and transmission of the virus, the high error rate associated with these reads can lead to inadequate genome assembly and downstream biological interpretation. In this study, we evaluate the accuracy and robustness of machine learning (ML) models using six different embedding techniques on SARS-CoV-2 error-incorporated genome sequences. Our analysis includes two types of error-incorporated genome sequences: those generated using simulation tools to emulate error profiles of long-read sequencing platforms and those generated by introducing random errors. We show that the spaced k-mers embedding method achieves high accuracy in classifying error-free SARS-CoV-2 genome sequences, and the spaced k-mers and weighted k-mers embedding methods are highly accurate in predicting error-incorporated sequences. The fixed-length vectors generated by these methods contribute to the high accuracy achieved. Our study provides valuable insights for researchers to effectively evaluate ML models and gain a better understanding of the approach for accurate identification of critical SARS-CoV-2 genome sequences.