Interactive transcriptome analyses of Northern Wild Rice (Zizania palustris L.) and Bipolaris oryzae show convoluted communications during the early stages of fungal brown spot development.

Fungal diseases, caused mainly by Bipolaris spp., are past and current threats to Northern Wild Rice (NWR) grain production and germplasm preservation in both natural and cultivated settings. Genetic resistance against the pathogen is scarce. Toward expanding our understanding of the global gene communications of NWR and Bipolaris oryzae interaction, we designed an RNA sequencing study encompassing the first 12 h and 48 h of their encounter. NWR activated numerous plant recognition receptors after pathogen infection, followed by active transcriptional reprogramming of signaling mechanisms driven by Ca2+ and its sensors, mitogen-activated protein kinase cascades, activation of an oxidative burst, and phytohormone signaling-bound mechanisms. Several transcription factors associated with plant defense were found to be expressed. Importantly, evidence of diterpenoid phytoalexins, especially phytocassane biosynthesis, among expression of other defense genes was found. In B. oryzae, predicted genes associated with pathogenicity including secreted effectors that could target plant defense mechanisms were expressed. This study uncovered the early molecular communication between the NWR-B. oryzae pathosystem, which could guide selection for allele-specific genes to boost NWR defenses, and overall aid in the development of more efficient selection methods in NWR breeding through the use of the most virulent fungal isolates.

Emerging Role of Autophagy in Governing Cellular Dormancy, Metabolic Functions, and Therapeutic Responses of Cancer Stem Cells.

Tumors are composed of heterogeneous populations of dysregulated cells that grow in specialized niches that support their growth and maintain their properties. Tumor heterogeneity and metastasis are among the major hindrances that exist while treating cancer patients, leading to poor clinical outcomes. Although the factors that determine tumor complexity remain largely unknown, several genotypic and phenotypic changes, including DNA mutations and metabolic reprograming provide cancer cells with a survival advantage over host cells and resistance to therapeutics. Furthermore, the presence of a specific population of cells within the tumor mass, commonly known as cancer stem cells (CSCs), is thought to initiate tumor formation, maintenance, resistance, and recurrence. Therefore, these CSCs have been investigated in detail recently as potential targets to treat cancer and prevent recurrence. Understanding the molecular mechanisms involved in CSC proliferation, self-renewal, and dormancy may provide important clues for developing effective therapeutic strategies. Autophagy, a catabolic process, has long been recognized to regulate various physiological and pathological processes. In addition to regulating cancer cells, recent studies have identified a critical role for autophagy in regulating CSC functions. Autophagy is activated under various adverse conditions and promotes cellular maintenance, survival, and even cell death. Thus, it is intriguing to address whether autophagy promotes or inhibits CSC functions and whether autophagy modulation can be used to regulate CSC functions, either alone or in combination. This review describes the roles of autophagy in the regulation of metabolic functions, proliferation and quiescence of CSCs, and its role during therapeutic stress. The review further highlights the autophagy-associated pathways that could be used to regulate CSCs. Overall, the present review will help to rationalize various translational approaches that involve autophagy-mediated modulation of CSCs in controlling cancer progression, metastasis, and recurrence.

A novel hybrid CNN and BiGRU-Attention based deep learning model for protein function prediction.

Proteins are the building blocks of all living things. Protein function must be ascertained if the molecular mechanism of life is to be understood. While CNN is good at capturing short-term relationships, GRU and LSTM can capture long-term dependencies. A hybrid approach that combines the complementary benefits of these deep-learning models motivates our work. Protein Language models, which use attention networks to gather meaningful data and build representations for proteins, have seen tremendous success in recent years processing the protein sequences. In this paper, we propose a hybrid CNN + BiGRU - Attention based model with protein language model embedding that effectively combines the output of CNN with the output of BiGRU-Attention for predicting protein functions. We evaluated the performance of our proposed hybrid model on human and yeast datasets. The proposed hybrid model improves the Fmax value over the state-of-the-art model SDN2GO for the cellular component prediction task by 1.9 %, for the molecular function prediction task by 3.8 % and for the biological process prediction task by 0.6 % for human dataset and for yeast dataset the cellular component prediction task by 2.4 %, for the molecular function prediction task by 5.2 % and for the biological process prediction task by 1.2 %.

Age-associated changes in the gut microbiome impact efficacy of tumor immunomodulatory treatments.

In-situ vaccination (ISV) utilizing nanoparticles (NPs) and therapeutic devices like focused ultrasound (FUS) can trigger immune-mediated killing of both treated and untreated cancer cells. However, the impact of confounding factors such as aging and gut microbiota composition on therapeutic outcomes remains poorly understood. In this study, we sequentially treated young mice (∼8 weeks) and old mice (>18 months) with bilateral melanoma using FUS and calreticulin nanoparticles (CRT-NP) to enhance immunogenic cell death. The combination of CRT-NP and FUS (CFUS) demonstrated greater efficacy in inducing regression of treated and untreated tumors in young mice compared to old mice. The diminished effectiveness in older mice was associated with significant differences in gut microbiome composition, characterized by alterations in bacterial species and splenic immune cells. Specifically, young mice exposed to CFUS exhibited higher abundance of Bacteroidetes and Verrucomicrobia, which was not observed in the aged cohorts. Turicibacter, Anaerotruncus, and Ruminiclostridium demonstrated negative correlations with CD8+ T cells but positive correlations with CD4+ T cells and MDSC cells in both age groups. Taxon set enrichment analysis revealed 58 significantly enriched host gene targets in the young cluster compared to only 11 in the aged cluster. These findings highlight the relationship between ISV treatment efficacy and gut microbiome composition, suggesting that interventions such as diet modification, probiotics, or fecal microbiota transplantation may hold potential as therapeutic strategies to enhance immune responses against solid tumors.

Treating methicillin-resistant Staphylococcus aureus (MRSA) bone infection with focused ultrasound combined thermally sensitive liposomes.

OBJECTIVE: Chronic bone infection caused by Staphylococcus aureus biofilms in children and adults is characterized by reduced antibiotic sensitivity. In this study, we assessed 'heat-targeted, on-demand' antibiotic delivery for S. aureus killing by combining ciprofloxacin (CIP)-laden low-temperature sensitive liposomes (LTSLs) with local high-intensity focused ultrasound (HIFU) induced bone heating in a rat model of bone infection. METHODS: CIP-LTSLs were prepared using the thin-film hydration and extrusion method. Bone infection was established by surgically implanting an orthopedic K-wire colonized with methicillin-resistant S. aureus (MRSA) strain into rat's femurs. For bone heating, ultrasound-guided HIFU exposures were performed to achieve a local temperature of 40-42 °C (∼15 min) concurrently with intravenous injection of CIP-LTSLs or CIP. CIP biodistribution was determined spectrophotometrically and therapeutic efficacy was determined by bacteriological, histological and scanning electron microscopy (SEM) analyses. RESULTS: CIP-LTSLs in the range of 183.5 nm ± 1.91 showed an encapsulation efficiency of >70% at 37 °C and a complete release at ∼42 °C. The metal implantation method yielded medullary osteomyelitis characterized by suppurative changes (bacterial and pus pockets) by day 10 in bones and adjoining muscle tissues. HIFU heating significantly improved CIP delivery from LTSLs in bones, resulting in a significant reduction in MRSA load compared to HIFU and CIP alone groups. These were also verified by histology and SEM, wherein a distinct reduction in S. aureus population in the infected metal wires and tissues from the combinatorial therapy was noted. CONCLUSION: HIFU improved CIP delivery to bones, achieving clearance of hard-to-treat MRSA biofilms.

Overcoming Resistance to Immune Checkpoint Inhibitor Therapy Using Calreticulin-Inducing Nanoparticle.

Nanoparticles (NPs) have the ability to transform poorly immunogenic tumors into activated 'hot' targets. In this study, we investigated the potential of a liposome-based nanoparticle (CRT-NP) expressing calreticulin as an in-situ vaccine to restore sensitivity to anti-CTLA4 immune checkpoint inhibitor (ICI) in CT26 colon tumors. We found that a CRT-NP with a hydrodynamic diameter of approximately 300 nm and a zeta potential of approximately +20 mV induced immunogenic cell death (ICD) in CT-26 cells in a dose-dependent manner. In the mouse model of CT26 xenograft tumors, both CRT-NP and ICI monotherapy caused moderate reductions in tumor growth compared to the untreated control group. However, the combination therapy of CRT-NP and anti-CTLA4 ICI resulted in remarkable suppression of tumor growth rates (>70%) compared to untreated mice. This combination therapy also reshaped the tumor microenvironment (TME), achieving the increased infiltration of antigen-presenting cells (APCs) such as dendritic cells and M1 macrophages, as well as an abundance of T cells expressing granzyme B and a reduction in the population of CD4+ Foxp3 regulatory cells. Our findings indicate that CRT-NPs can effectively reverse immune resistance to anti-CTLA4 ICI therapy in mice, thereby improving the immunotherapeutic outcome in the mouse model.

Enabling Chemo-Immunotherapy with HIFU in Canine Cancer Patients.

High intensity focused ultrasound (HIFU) is a promising non-invasive technique for treating solid tumors using thermal and histotripsy-based mechanical ablation. However, its clinical significance in different tumor types is not fully understood. To assess its therapeutic efficacy and immunomodulatory properties, we compared HIFU thermal ablation and histotripsy ablation in dogs with spontaneous tumors. We also evaluated the ability of non-ablative HIFU-based mild hyperthermia (40-45 ºC) to improve Doxorubicin delivery and immunomodulation. Our results showed that HIFU thermal ablation induced tumor remission in the majority of treated patients over 60 days, while histotripsy achieved partial response to stable disease persistence. The adverse effects of thermal ablation were minor to moderate, while histotripsy exposures were relatively well-tolerated. Furthermore, we observed a correlation between HIFU-therapeutic response and serum anti-tumor cytokine profiles and the presence of functionally active cytotoxic immune cells in patients. Similarly, Doxorubicin-treated patients showed improved drug delivery, efficacy, and anti-tumor immune responses with HIFU hyperthermia. In conclusion, our study demonstrates that depending on the tumor type and treatment parameters, HIFU treatments can enable tumor growth control, immune activation, and chemotherapy in veterinary patient. These findings have significant clinical implications and highlight the potential of HIFU as a promising cancer treatment approach.

Adrenergic receptor signaling regulates the CD40-receptor mediated anti-tumor immunity.

Inroduction: Anti-CD40 agonistic antibody (αCD40), an activator of dendritic cells (DC) can enhance antigen presentation and activate cytotoxic T-cells against poorly immunogenic tumors. However, cancer immunotherapy trials also suggest that αCD40 is only moderately effective in patients, falling short of achieving clinical success. Identifying factors that decrease αCD40 immune-stimulating effects can aid the translation of this agent to clinical reality. Method/Results: Here, we reveal that ß-adrenergic signaling on DCs directly interferes with αCD40 efficacy in immunologically cold head and neck tumor model. We discovered that ß-2 adrenergic receptor (ß2AR) activation rewires CD40 signaling in DCs by directly inhibiting the phosphorylation of IκBα and indirectly by upregulating levels of phosphorylated-cAMP response element-binding protein (pCREB). Importantly, the addition of propranolol, a pan ß-Blocker reprograms the CD40 pathways, inducing superior tumor regressions, increased infiltration of cytotoxic T-cells, and a reduced burden of regulatory T-cells in tumors compared to monotherapy. Discussion/Conclusion: Thus, our study highlights an important mechanistic link between stress-induced ß2AR signaling and reduced αCD40 efficacy in cold tumors, providing a new combinatorial approach to improve clinical outcomes in patients.

Review of Robot-Assisted HIFU Therapy.

This paper provides an overview of current robot-assisted high-intensity focused ultrasound (HIFU) systems for image-guided therapies. HIFU is a minimally invasive technique that relies on the thermo-mechanical effects of focused ultrasound waves to perform clinical treatments, such as tumor ablation, mild hyperthermia adjuvant to radiation or chemotherapy, vein occlusion, and many others. HIFU is typically performed under ultrasound (USgHIFU) or magnetic resonance imaging guidance (MRgHIFU), which provide intra-operative monitoring of treatment outcomes. Robot-assisted HIFU probe manipulation provides precise HIFU focal control to avoid damage to surrounding sensitive anatomy, such as blood vessels, nerve bundles, or adjacent organs. These clinical and technical benefits have promoted the rapid adoption of robot-assisted HIFU in the past several decades. This paper aims to present the recent developments of robot-assisted HIFU by summarizing the key features and clinical applications of each system. The paper concludes with a comparison and discussion of future perspectives on robot-assisted HIFU.

Lite-SeqCNN: A Light-Weight Deep CNN Architecture for Protein Function Prediction.

The short-and-long range interactions amongst amino-acids in a protein sequence are primarily responsible for the function performed by the protein. Recently convolutional neural network (CNN)s have produced promising results on sequential data including those of NLP tasks and protein sequences. However, CNN's strength primarily lies at capturing short range interactions and are not so good at long range interactions. On the other hand, dilated CNNs are good at capturing both short-and-long range interactions because of varied - short-and-long - receptive fields. Further, CNNs are quite light-weight in terms of trainable parameters, whereas most existing deep learning solutions for protein function prediction (PFP) are based on multi-modality and are rather complex and heavily parametrized. In this paper, we propose a (sub-sequence + dilated-CNNs)-based simple, light-weight and sequence-only PFP framework Lite-SeqCNN. By varying dilation-rates, Lite-SeqCNN efficiently captures both short-and-long range interactions and has (0.50-0.75 times) fewer trainable parameters than its contemporary deep learning models. Further, Lite-SeqCNN + is an ensemble of three Lite-SeqCNNs developed with different segment-sizes that produces even better results compared to the individual models. The proposed architecture produced improvements upto 5% over state-of-the-art approaches Global-ProtEnc Plus, DeepGOPlus, and GOLabeler on three different prominent datasets curated from the UniProt database.

Immunomodulation and targeted drug delivery with high intensity focused ultrasound (HIFU): Principles and mechanisms.

High intensity focused ultrasound (HIFU) is a non-invasive and non-ionizing sonic energy-based therapeutic technology for inducing thermal and non-thermal effects in tissues. Depending on the parameters, HIFU can ablate tissues by heating them to >55 °C to induce denaturation and coagulative necrosis, improve radio- and chemo-sensitizations and local drug delivery from nanoparticles at moderate hyperthermia (∼41-43 °C), and mechanically fragment cells using acoustic cavitation (also known as histotripsy). HIFU has already emerged as an attractive modality for treating human & veterinary cancers, infectious diseases, and neuromodulation. Herein, we comprehensively review the role of HIFU in enhancing drug delivery and immunomodulation in soft and calcified tissues. Specifically, the ability of HIFU to improve adjuvant treatments from various classes of therapeutic agents are described. These crucial insights highlight the opportunities and challenges of HIFU technology and its potential to support new clinical trials and translation to patients.

Optimization of test day for milk yield recording and sire evaluation in Murrah buffaloes.

In the present study, random regression models (RRM) were used to estimate genetic parameters for test-day milk yield in Murrah buffaloes using Legendre polynomial function (LP), with the objective to find the best combination of "minimum test-day model," which would be essential and sufficient to evaluate the trait successfully. Data included for analysis were 10,615 first lactation monthly test-day milk yield records (5th, 35th, 65th, …, 305th) from 965 Murrah buffaloes for the period 1975-2018. Cubic to octic-order orthogonal polynomials with homogeneous residual variances were used for the estimation of genetic parameters. Random regression models with sixth-order were selected based on goodness of fit criteria like lower AIC, BIC and residual variance. Heritability estimates ranged from 0.079 (TD6) to 0.21(TD10). For both ends of lactation, the additive genetic and environmental variances were higher and ranged from 0.21 ± 0.12 (TD6) to 0.85 ± 0.35 kg2 (TD1) and 3.74 ± 0.36 (TD11) to 1.36 ± 0.14 kg2 (TD9), respectively. Between adjacent test-day records, genetic correlation estimates ranged from 0.09 ± 0.31 (TD1 and TD2) to 0.97 ± 0.03 (TD3 and TD4; TD4 and TD5), but values gradually declined as the distance between test days increased. Negative genetic correlations were also obtained between TD1 with TD3 to TD9, TD2 with TD9 and TD10, and TD3 with TD10. On the basis of genetic correlations, models with 5 and/or 6 test-days combination were able to account for 86.1%-98.7% of variation along the lactation. Models with fourth and fifth-order LP functions were considered to account for variance with combinations of 5 and/or 6 test-day milk yields. The model with 6 test-day combinations had a higher rank correlation (0.93) with model using 11 monthly test-day milk yield records. On the basis of relative efficiency, the model with 6 monthly test day combinations with fifth-order was more efficient (maximum 99%) than the model using 11 monthly test-day milk yield records. Looking into the similar accuracy with the 11TD model, and the low resources requirement, we recommend the use of the "6 test-day combination model" for sire evaluation. These models may help in reducing the cost and time for data recording of milk yield.

MCWS-Transformers: Towards an Efficient Modeling of Protein Sequences via Multi Context-Window Based Scaled Self-Attention.

This paper advances the self-attention mechanism in the standard transformer network specific to the modeling of the protein sequences. We introduce a novel context-window based scaled self-attention mechanism for processing protein sequences that is based on the notion of (i) local context and (ii) large contextual pattern. Both notions are essential to building a good representation for protein sequences. The proposed context-window based scaled self-attention mechanism is further used to build the multi context-window based scaled (MCWS) transformer network for the protein function prediction task at the protein sub-sequence level. Overall, the proposed MCWS transformer network produced improved predictive performances, outperforming existing state-of-the-art approaches by substantial margins. With respect to the standard transformer network, the proposed network produced improvements in F1-score of +2.30% and +2.08% on the biological process (BP) and molecular function (MF) datasets, respectively. The corresponding improvements over the state-of-the-art ProtVecGen-Plus+ProtVecGen-Ensemble approach are +3.38% (BP) and +2.86% (MF). Equally important, robust performances were obtained across protein sequences of different lengths.

A Sub-Sequence Based Approach to Protein Function Prediction via Multi-Attention Based Multi-Aspect Network.

Inferring the protein function(s) via the protein sub-sequence classification is often obstructed due to lack of knowledge about function(s) of sub-sequences in the protein sequence. In this regard, we develop a novel "multi-aspect" paradigm to perform the sub-sequence classification in an efficient way by utilizing the information of the parent sequence. The aspects are: (1) Multi-label: independent labelling of sub-sequences with more than one functions of the parent sequence, and (ii) Label-relevance: scoring the parent functions to highlight the relevance of performing a given function by the sub-sequence. The multi-aspect paradigm is used to propose the "Multi-Attention Based Multi-Aspect Network" for classifying the protein sub-sequences, where multi-attention is a novel approach to process sub-sequences at word-level. Next, the proposed Global-ProtEnc method is a sub-sequence based approach to encoding protein sequences for protein function prediction task, which is finally used to develop as ensemble methods, Global-ProtEnc-Plus. Evaluations of both the Global-ProtEnc and the Global-ProtEnc-Plus methods on the benchmark CAFA3 dataset delivered a outstanding performances. Compared to the state-of-the-art DeepGOPlus, the improvements in Fmax with the Global-ProtEnc-Plus for the biological process is +6.50 percent and cellular component is +1.90 percent.

Estimation of genetic parameters of growth traits for direct and maternal effects in Murrah buffalo.

An understanding of genetic principles and environmental factors affecting the growth traits is essential to implement optimal breeding and selection programs. Early growth is an indicator of future success in production and reproduction status of dairy animals. In this study, a total of 18,989 records of body weight were used to estimate genetic parameters of body weight at birth (BW), 3 months (3BW), 6 months (6BW), 9 months (9BW),12 months (12BW), 18 months (18BW), 24 months (24 BW), 30 months (3BW), and 36 months (36BW) in Murrah buffalo at ICAR-NDRI Karnal, Haryana, for the period 1974-2019. The genetic parameters were estimated using the average information restricted maximum likelihood (AIREML) procedure by excluding or including maternal effects. Six analytical models were fitted in order to optimize the model for each trait. The most appropriate univariate model was selected based on the log likelihood ratio test (LRT). Influencing factors like calf sex, period of birth, season of birth, and dam's parity were investigated. The results showed that the maternal genetic effects, in addition to direct additive effects, were important for unbiased and accurate genetic parameter estimates of growth traits in Murrah buffaloes. Total heritability estimates h2T1 for BW, 3BW, 6BW, 9BW, 12BW, 18BW, 24BW, 30BW, and 36BW were 0.25, 0.04, 0.14, 0.16, 0.10, 0.15, 0.21, 0.24, and 0.23, respectively. Maternal effect was significant for birth weight and accounted for 13% variation through maternal genetic and 5% variability through maternal permanent environmental effect. Maternal genetic effect was also important for other traits. However, it interfered with the estimates of variance ratios in live weight traits owing to large and negative covariance between direct and maternal genetic effects. Direct genetic correlations between body weight traits were positive and high ranging from 0.10 to 0.94. Results revealed that the Murrah herd has a sizable genetic variability for growth traits and hence, there is sufficient scope for selection for achieving better growth rate if selection in this direction is applied. Owing to higher positive genetic correlation of 6BW with later ages, the scope of indirect selection for optimum growth in later ages can be aimed at by selecting animals for their higher 6-month live weight.

High-throughput platform for yeast morphological profiling predicts the targets of bioactive compounds.

Morphological profiling is an omics-based approach for predicting intracellular targets of chemical compounds in which the dose-dependent morphological changes induced by the compound are systematically compared to the morphological changes in gene-deleted cells. In this study, we developed a reliable high-throughput (HT) platform for yeast morphological profiling using drug-hypersensitive strains to minimize compound use, HT microscopy to speed up data generation and analysis, and a generalized linear model to predict targets with high reliability. We first conducted a proof-of-concept study using six compounds with known targets: bortezomib, hydroxyurea, methyl methanesulfonate, benomyl, tunicamycin, and echinocandin B. Then we applied our platform to predict the mechanism of action of a novel diferulate-derived compound, poacidiene. Morphological profiling of poacidiene implied that it affects the DNA damage response, which genetic analysis confirmed. Furthermore, we found that poacidiene inhibits the growth of phytopathogenic fungi, implying applications as an effective antifungal agent. Thus, our platform is a new whole-cell target prediction tool for drug discovery.

An Ensemble Tf-Idf Based Approach to Protein Function Prediction via Sequence Segmentation.

This paper explores the use of variants of tf-idf-based descriptors, namely length-normalized-tf-idf and log-normalized-tf-idf, combined with a segmentation technique, for efficient modeling of variable-length protein sequences. The proposed solution, ProtVecGen-Ensemble, is an ensemble of three models trained on differently segmented datasets constructed from an input dataset containing complete protein sequences. Evaluations using biological process (BP) and molecular function (MF) datasets demonstrate that the proposed feature set is not only superior to its contemporaries but also produces more consistent results with respect to variation in sequence lengths. Improvements of +6.07% (BP) and +7.56% (MF) over state-of-the-art tf-idf-based MLDA feature set were obtained. The best results were achieved when ProtVecGen-Ensemble was combined with ProtVecGen-Plus - the state-of-the-art method for protein function prediction - resulting in improvements of +8.90% (BP) and +11.28% (MF) over MLDA and +1.49% (BP) and +2.07% (MF) over ProtVecGen-Plus+MLDA. To capture the performance consistency with respect to sequence lengths, we have defined a variance-based metric, with lower values indicating better performance. On this metric, the proposed ProtVecGen-Ensemble+ProtVecGen-Plus framework resulted in reductions of 56.85 percent (BP) and 56.08 percent (MF) over MLDA and 10.37 percent (BP) and 26.48 percent (MF) over ProtVecGenPlus+MLDA.

A multilayer network-enabled ultrasonic image series analysis approach for online cancer drug delivery monitoring.

The objective of this study is to develop an effective data-driven methodology for the online monitoring of cancer drug delivery guided by the ultrasonic images. To achieve this goal, effective image quantification and accurate feature extraction play a critical role on image-guided drug delivery (IGDD) monitoring. However, the existing image-guided approaches in such area are mainly focused on the analysis for individual images rather than the image series. In fact, the temporal patterns between consecutive images may contain critical information and it is necessary to be considered in the monitoring analysis. In addition, the conventional approaches, such as the pure intensity-based method, also do not sufficiently consider the effects of noise in the ultrasonic images, which also limits the monitoring sensitivity and accuracy. To address the challenges, this paper proposed a novel multilayer network-enabled IGDD (MNE-IGDD) monitoring approach. The contributions of the proposed method can be summarized into three aspects: (1) formulate the sequential ultrasound images to a multilayer network by the proposed spatial-regularized distance; (2) detect drug delivery area based on community detection algorithm of multilayer network; and (3) quantify the drug delivery progress by incorporating the image intensity-based features with the detected community. Both the detected communities and feature increment percentages are applied as the evaluation metric for validation. A simulation study was conducted and this method was also applied to a real-world mouse colon tumor treatment case study under three temperature conditions. Both simulation and the real-world case studies demonstrated that the proposed method is promising to achieve satisfactory monitoring performance in clinical trials.

Effects of carbamate pesticides intermediates on Escherichia coli membrane architecture: An in vitro and in silico approach.

Methyl isocyanate (MIC), a low molecular weight synthetic aliphatic compound, having an isocyanate group (-NCO), has industrial application. In this study, the effects of methyl isocyanate and its mechanism on outer membrane protein of Escherichia coli were observed using experimental and computational methods. In vitro exposure of N-succinimidyl N-methylcarbamate (NSNM) a synthetic analogue of MIC on E. coli to a final concentration of 2 mM was found to affect the growth curve pattern and changes in cell morphology. Molecular docking studies of MIC and NSNM with E. coli outer membrane protein (OmpW, OmpX, OmpF OmpA), and periplasmic domain (PAL) were performed. The in-silico results revealed that outer membrane protein OmpF showed the highest negative binding energy, i.e. ∆G -4.11 kcal/mole and ∆G -3.19 kcal/mole by NSNM and MIC as compared to other proteins. Our study concludes that methyl isocyanate retains lethal toxicity which leads to cell death due to the membrane protein damage of E. coli membrane.

Host-Induced Gene Silencing of a Sclerotinia sclerotiorum oxaloacetate acetylhydrolase Using Bean Pod Mottle Virus as a Vehicle Reduces Disease on Soybean.

A lack of complete resistance in the current germplasm complicates the management of Sclerotinia stem rot (SSR) caused by Sclerotinia sclerotiorum in soybean. In this study, we used bean pod mottle virus (BPMV) as a vehicle to down-regulate expression of a key enzyme in the production of an important virulence factor in S. sclerotiorum, oxalic acid (OA). Specifically, we targeted a gene encoding oxaloacetate acetylhydrolase (Ssoah1), because Ssoah1 deletion mutants are OA deficient and non-pathogenic on soybean. We first established that S. sclerotiorum can uptake environmental RNAs by monitoring the translocation of Cy3-labeled double-stranded and small interfering RNA (ds/siRNAs) into fungal hyphae using fluorescent confocal microscopy. This translocation led to a significant decrease in Ssoah1 transcript levels in vitro. Inoculation of soybean plants with BPMV vectors targeting Ssoah1 (pBPMV-OA) also led to decreased expression of Ssoah1. Importantly, pBPMV-OA inoculated plants showed enhanced resistance to S. sclerotiorum compared to empty-vector control plants. Our combined results provide evidence supporting the use of HIGS and exogenous applications of ds/siRNAs targeting virulence factors such as OA as viable strategies for the control of SSR in soybean and as discovery tools that can be used to identify previously unknown virulence factors.