GC-MS based antioxidants characterization in Saussurea heteromalla (D. Don) Hand-Mazz by inhibition of nitric oxide generation in macrophages.

For centuries, medicinal plants have served as the cornerstone for traditional health care systems and same practice is still prevalent today. In the Himalayan region, Saussurea heteromalla holds a significant place in traditional medicine and is used to address various health issues. Despite its historical use, little exploration has focused on its potential for scavenging free radicals and reducing inflammation. Hence, our current study aims to investigate the free radical scavenging capabilities of S. heteromalla extracts. The n-hexane extract of entire plant revealed promising activity. This extract underwent extensive extraction on a larger scale. Subsequent purification, employing column chromatography, HPLC-DAD techniques, led to the identification of active compounds, confirmed via GC-MS and the NIST database as 1-O-butyl 2-O-octyl benzene-1,2-dicarboxylate and 2,4-ditert-butylphenol. Assessing the free radical scavenging properties involved utilizing RAW-264.7 macrophages activated by lipopolysaccharides. Notably, the compound 2,4-di-tert-butylphenol exhibited remarkable scavenging abilities, demonstrating over 80% inhibition of Nitric oxide. This study stands as the inaugural report on the isolation of these compounds from S. heteromalla.

3D-IncNet: Head and Neck (H&N) Primary Tumors Segmentation and Survival Prediction.

Cancer begins when healthy cells change and grow out of control, forming a mass called a tumor. Head and neck (H&N) cancers usually develop in or around the head and neck, including the mouth (oral cavity), nose and sinuses, throat (pharynx), and voice box (larynx). 4% of all cancers are H&N cancers with a very low survival rate (a five-year survival rate of 64.7%). FDG-PET/CT imaging is often used for early diagnosis and staging of H&N tumors, thus improving these patients' survival rates. This work presents a novel 3D-Inception-Residual aided with 3D depth-wise convolution and squeeze and excitation block. We introduce a 3D depth-wise convolution-inception encoder consisting of an additional 3D squeeze and excitation block and a 3D depth-wise convolution-based residual learning decoder (3D-IncNet), which not only helps to recalibrate the channel-wise features but adaptively through explicit inter-dependencies modeling but also integrate the coarse and fine features resulting in accurate tumor segmentation. We further demonstrate the effectiveness of inception-residual encoder-decoder architecture in achieving better dice scores and the impact of depth-wise convolution in lowering the computational cost. We applied random forest for survival prediction on deep, clinical, and radiomics features. Experiments are conducted on the benchmark HECKTOR21 challenge, which showed significantly better performance by surpassing the state-of-the-artwork and achieved 0.836 and 0.811 concordance index and dice scores, respectively. We made the model and code publicly available.

Health-promoting compounds in Amomum villosum Lour and Amomum tsao-ko: Fruit essential oil exhibiting great potential for human health.

The Zingiberaceae family serves as a diverse repository of bioactive phytochemicals, comprising approximately 52 genera and 1300 species of aromatic perennial herbs distinguished by their distinct creeping horizontal or tuberous rhizomes. Amomum villosum Lour. and Amomum tsao-ko Crevost & Lemaire., are the important plants of family Zingiberaceae that have been widely used in traditional medicine for the treatment of many ailments. The Amomum species are employed for their aromatic qualities and are valued as spices and flavorings. In the essential oils (EOs) of Amomum species, notable constituents include, camphor, methyl chavicol, bornyl acetate, trans-p-(1-butenyl) anisole, α-pinene, and ß-pinene. OBJECTIVE: The aim of this review is to present an overview of pharmacological studies pertaining to the extracts and secondary metabolites isolated from both species. The foremost objective of review is not only to increase the popularity of Amomum as a healthy food choice but also to enhance its status as a staple ingredient for the foreseeable future. RESULT: We endeavored to gather the latest information on antioxidant, antidiabetic, anticancer, antiobesity, antimicrobial, and anti-inflammatory properties of plants as well as their role in neuroprotective diseases. Research conducted through in-vitro studies, animal model, and compounds analysis have revealed that both plants exhibit a diverse array health promoting properties. CONCLUSION: the comprehensive review paper provides valuable insights into the diverse range of bioactive phytochemicals found in A. villosum and A. tsao-ko, showcasing their potential in preventing diseases and promoting overall human well-being. The compilation of information on their various health-enhancing properties contributes to the broader understanding of these plants and their potential applications in traditional medicine and beyond.

Molecular docking of bioactive compounds extracted and purified from selected medicinal plant species against covid-19 proteins and in vitro evaluation.

Bioactive compounds are secondary metabolites of plants. They offer diverse pharmacological properties. Peganum harmala is reported to have pharmaceutical effects like insecticidal, antitumor, curing malaria, anti-spasmodic, vasorelaxant, antihistaminic effect. Rosa brunonii has medicinal importance in its flower and fruits effective against different diseases and juice of leaf is reported to be applied externally to cure wounds and cuts. Dryopteris ramosa aqueous leaf extract is used to treat stomach ulcers and stomachaches. Each of these three medicinal plants have been indicated to have anticancer, antiviral, antioxidant, cytotoxic and antifungal effects but efficacy of their bioactive compounds remained unexplored. Study was aimed to explore In-vitro and In-silico anticancer, antiviral, antioxidant, cytotoxic and antifungal effects of bioactive compounds of above three medicinal plants. DPPH and ABTS assay were applied for assessment of antioxidant properties of compounds. Antibacterial properties of compounds were checked by agar well diffusion method. Brine shrimp lethality assay was performed to check cytotoxic effect of compounds. Molecular docking was conducted to investigate the binding efficacy between isolated compounds and targeted proteins. The compound isomangiferrin and tiliroside presented strong antioxidant potential 78.32% (± 0.213) and 77.77% (± 0.211) respectively in DPPH assay while harmaline showed 80.71% (± 0.072) at 200 µg/mL in ABTS assay. The compound harmine, harmaline and PH-HM 17 exhibited highest zone of inhibition 22 mm, 23 mm, 22 mm respectively against Xanthomonas while Irriflophenone-3-C-ß- D-glucopyranoside showed maximum zone of inhibition 34 mm against E. coli. The compound isomangiferrin and vasicine contained strong antibacterial activity 32 mm and 22 mm respectively against S. aureus. The compound mangiferrin, astragalin, tiliroside, quercitin-3-O-rhamnoside showed maximum inhibitory zone 32 mm, 26 mm, 24 mm and 22 mm respectively against Klebsiella pneumoniae. Highest cytotoxic effect was observed by compound tiliroside i.e. 95% with LD50 value 73.59 µg/mL. The compound tiliroside showed the best binding mode of interaction to all targeted proteins presenting maximum hydrophobic interactions and hydrogen bonds. The binding affinity of tiliroside was - 17.9, - 14.9, - 14.6, - 13.8, - 12.8 against different proteins 6VAR, 5C5S, IEA3, 2XV7 and 6LUS respectively. Bioactive compounds are significant natural antioxidants, which could help to prevent the progression of various diseases caused by free radicals. Based on molecular docking we have concluded that phytochemicals can have better anticancer and antiviral potential.

Placental vessel segmentation and registration in fetoscopy: Literature review and MICCAI FetReg2021 challenge findings.

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. Computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. For the segmentation task, overall baseline performed was the top performing (aggregated mIoU of 0.6763) and was the best on the vessel class (mIoU of 0.5817) while team RREB was the best on the tool (mIoU of 0.6335) and fetus (mIoU of 0.5178) classes. For the registration task, overall the baseline performed better than team SANO with an overall mean 5-frame SSIM of 0.9348. Qualitatively, it was observed that team SANO performed better in planar scenarios, while baseline was better in non-planner scenarios. The detailed analysis showed that no single team outperformed on all 6 test fetoscopic videos. The challenge provided an opportunity to create generalized solutions for fetoscopic scene understanding and mosaicking. In this paper, we present the findings of the FetReg2021 challenge, alongside reporting a detailed literature review for CAI in TTTS fetoscopy. Through this challenge, its analysis and the release of multi-center fetoscopic data, we provide a benchmark for future research in this field.

Two-Stage Self-Supervised Contrastive Learning Aided Transformer for Real-Time Medical Image Segmentation.

The availability of large, high-quality annotated datasets in the medical domain poses a substantial challenge in segmentation tasks. To mitigate the reliance on annotated training data, self-supervised pre-training strategies have emerged, particularly employing contrastive learning methods on dense pixel-level representations. In this work, we proposed to capitalize on intrinsic anatomical similarities within medical image data and develop a semantic segmentation framework through a self-supervised fusion network, where the availability of annotated volumes is limited. In a unified training phase, we combine segmentation loss with contrastive loss, enhancing the distinction between significant anatomical regions that adhere to the available annotations. To further improve the segmentation performance, we introduce an efficient parallel transformer module that leverages Multiview multiscale feature fusion and depth-wise features. The proposed transformer architecture, based on multiple encoders, is trained in a self-supervised manner using contrastive loss. Initially, the transformer is trained using an unlabeled dataset. We then fine-tune one encoder using data from the first stage and another encoder using a small set of annotated segmentation masks. These encoder features are subsequently concatenated for the purpose of brain tumor segmentation. The multiencoder-based transformer model yields significantly better outcomes across three medical image segmentation tasks. We validated our proposed solution by fusing images across diverse medical image segmentation challenge datasets, demonstrating its efficacy by outperforming state-of-the-art methodologies.

Effective Approaches to Fetal Brain Segmentation in MRI and Gestational Age Estimation by Utilizing a Multiview Deep Inception Residual Network and Radiomics.

To completely comprehend neurodevelopment in healthy and congenitally abnormal fetuses, quantitative analysis of the human fetal brain is essential. This analysis requires the use of automatic multi-tissue fetal brain segmentation techniques. This paper proposes an end-to-end automatic yet effective method for a multi-tissue fetal brain segmentation model called IRMMNET. It includes a inception residual encoder block (EB) and a dense spatial attention (DSAM) block, which facilitate the extraction of multi-scale fetal-brain-tissue-relevant information from multi-view MRI images, enhance the feature reuse, and substantially reduce the number of parameters of the segmentation model. Additionally, we propose three methods for predicting gestational age (GA)-GA prediction by using a 3D autoencoder, GA prediction using radiomics features, and GA prediction using the IRMMNET segmentation model's encoder. Our experiments were performed on a dataset of 80 pathological and non-pathological magnetic resonance fetal brain volume reconstructions across a range of gestational ages (20 to 33 weeks) that were manually segmented into seven different tissue categories. The results showed that the proposed fetal brain segmentation model achieved a Dice score of 0.791±0.18, outperforming the state-of-the-art methods. The radiomics-based GA prediction methods achieved the best results (RMSE: 1.42). We also demonstrated the generalization capabilities of the proposed methods for tasks such as head and neck tumor segmentation and the prediction of patients' survival days.

Isolation, Characterization and Anticancer Activity of Two Bioactive Compounds from Arisaema flavum (Forssk.) Schott.

Medicinal plants play important role in the public health sector worldwide. Natural products from medicinal plants are sources of unlimited opportunities for new drug leads because of their unique chemical diversity. Researchers have focused on exploring herbal products as potential sources for the treatment of cancer, cardiac and infectious diseases. Arisaema flavum (Forssk.) is an important medicinal plant found in the northwest Himalayan regions of Pakistan. It is a poisonous plant and is used as a remedy against snake bites and scorpion stings. In this study, two bioactive compounds were isolated from Arisaema flavum (Forssk.) and their anticancer activity was evaluated against human breast cancer cell line MCF-7 using an MTT assay. The crude extract of Arisaema flavum (Forssk.) was subjected to fractionation using different organic solvents in increasing order of polarity. The fraction indicating maximum activity was then taken for isolation of bioactive compounds using various chromatographic and spectroscopic techniques such as column chromatography, thin-layer chromatography (TLC), gas chromatography−mass spectrometry (GC-MS), Fourier transform infrared spectroscopy (FTIR) and nuclear magnetic resonance spectroscopy (NMR). Crude extract of Arisaema flavum (Forssk.), as well as various fractions extracted in different solvents such as n-hexane, chloroform and ethyl acetate, were tested against human breast cancer cell line MCF-7 using an MTT assay. The crude extract exhibited significant dose-dependent anticancer activity with a maximum activity of 78.6% at 500 µg/mL concentration. Two compounds, hexadecanoic acid ethyl ester with molecular formula C18H36O7 and molar mass 284 and 5-Oxo-19 propyl-docosanoic acid methyl ester with molecular formula C26H50O3 and molecular mass 410, were isolated from chloroform fraction. These compounds were tested against the MCF-7cell line for cytotoxic activity and exhibited a significant (p < 0.00l) decrease in cell numbers for MCF-7 cells with IC50 of 25 µM after 48 h of treatment. Results indicated that Arisaema flavum (Forssk.) possesses compounds with cytotoxic activity that can further be exploited to develop anticancer formulations.

Nickel toxicity alters growth patterns and induces oxidative stress response in sweetpotato.

Nickel (Ni) contaminated soil is a persistent risk to plant growth and production worldwide. Therefore, to explore the Ni toxicity levels in sweetpotato production areas, we investigated the influence of different Ni treatments (0, 7.5, 15, 30, and 60 mg L-1) for 15 days on phenotype, Ni uptake, relative water content, gas exchange, photosynthetic pigments, oxidative stress, osmolytes, antioxidants, and enzymes of sweetpotato plants. The results presented that Ni at higher levels (30 and 60 mg L-1) substantially reduced growth, biomass, and root morphological traits. The Pearson correlation analysis suggested that Ni toxicity causes oxidative injuries as persistent augmentation of hydrogen peroxide (H2O2) and malonaldehyde (MDA) and reduced RWC, gas exchange, and photosynthetic pigment. Furthermore, this study revealed that sweetpotato could tolerate moderate Ni treatment (up to 15 mg L-1) by reducing oxidative stress. The results also indicated that the increase in the activities of mentioned osmolytes, antioxidants, and enzymes is not sufficient to overcome the higher Ni toxicity. Based on these results, we suggest using low Ni-contaminated soil for better growth of sweetpotato and also could be used as a phytoremediator in moderate Ni-contaminated soil.

Biorational Control of Callosobruchus maculatus (Coleoptera: Buchidae) in Stored Grains with Botanical Extracts.

Globally, around 2000 plant species are used against pest control. The utilization of botanicals is considered the most economic and biodegradable methods for the control of stored grains pests. Therefore, the current study was carried out to investigate the repellency potential of five botanicals against Callosbruchus maculatus F. in Haripur, Pakistan. The concentrations of Azadirachta indica L., Nicotiana tabacum L., Melia azedarach L., Nicotiana rustica L., and Thuja orientalis L. were, i.e., 0.5, 1.0, 1.5, 2.0, 2.5, and 3.0% in four replicates to establish contact effects. The data were recorded after 1, 2, 3, 6, 24, 48, 72, and 96 hours. The repellency effect of these plant species against C. maculatus were increased in both the time- and dose-dependent manner, and highest effect was observed at 72 h. In addition, the repellency effect was 91% for A. indica (class: V), 86% M. azedarach, 82%, N. tabacum (class: V), 79% N. rustica (class: IV), and 75% T. orientalis (class: IV) at 3% concentration against C. maculatus. Furthermore, following 96 hours' exposure to treatment the sensitivity response of insects decreases as the time interval increases, i.e., 86% A. indica (class: V) was followed by 71% M. azedarach (class: IV), 65% N. tabacum (class: IV), 61% N. rustica (class: IV), and T. orientalis 57% (class: III) repellency at highest concentration of 3%. The current study concluded that A. indica and M. azedarach can be incorporated for the management of C. maculatus and these plant species might be helpful in the productions of new biopesticides.

Effective multiscale deep learning model for COVID19 segmentation tasks: A further step towards helping radiologist.

Infection by the SARS-CoV-2 leading to COVID-19 disease is still rising and techniques to either diagnose or evaluate the disease are still thoroughly investigated. The use of CT as a complementary tool to other biological tests is still under scrutiny as the CT scans are prone to many false positives as other lung diseases display similar characteristics on CT scans. However, fully investigating CT images is of tremendous interest to better understand the disease progression and therefore thousands of scans need to be segmented by radiologists to study infected areas. Over the last year, many deep learning models for segmenting CT-lungs were developed. Unfortunately, the lack of large and shared annotated multicentric datasets led to models that were either under-tested (small dataset) or not properly compared (own metrics, none shared dataset), often leading to poor generalization performance. To address, these issues, we developed a model that uses a multiscale and multilevel feature extraction strategy for COVID19 segmentation and extensively validated it on several datasets to assess its generalization capability for other segmentation tasks on similar organs. The proposed model uses a novel encoder and decoder with a proposed kernel-based atrous spatial pyramid pooling module that is used at the bottom of the model to extract small features with a multistage skip connection concatenation approach. The results proved that our proposed model could be applied on a small-scale dataset and still produce generalizable performances on other segmentation tasks. The proposed model produced an efficient Dice score of 90% on a 100 cases dataset, 95% on the NSCLC dataset, 88.49% on the COVID19 dataset, and 97.33 on the StructSeg 2019 dataset as compared to existing state-of-the-art models. The proposed solution could be used for COVID19 segmentation in clinic applications. The source code is publicly available at https://github.com/RespectKnowledge/Mutiscale-based-Covid-_segmentation-usingDeep-Learning-models.

Influence of Blanching on the Gene Expression Profile of Phenylpropanoid, Flavonoid and Vitamin Biosynthesis, and Their Accumulation in Oenanthe javanica.

Field blanching is a process used in agriculture to obtain sweet, delicious, and tender stems of water dropwort by obstructing sunlight. The nutritional and transcriptomic profiling of blanched water dropwort has been investigated in our previous studies. However, the effect of blanching on the production of secondary metabolites and different vitamins in water dropwort has not been investigated at the transcriptomic level. This study explored the transcriptomic variations in the phenylpropanoid biosynthesis, flavonoid biosynthesis, and different vitamin biosynthesis pathways under different blanching periods in the water dropwort stems (pre-blanching, mid-blanching, post-blanching, and control). The results show that polyphenol and flavonoid contents decreased; however, the contents of vitamins (A, B1, B2, and C) and antioxidant activity increased significantly after blanching. Furthermore, the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of blanched water dropwort showed the downregulation of many important genes involved in phenylpropanoid and flavonoid biosynthesis pathways, and the downregulation of these genes might be the reason for the reduction in polyphenol and flavonoid contents. We also examined and highlighted the genes involved in the higher vitamin content, antioxidant activity, pale color, tenderness, and sweetness of the blanched stem of water dropwort. In conclusion, the present study explored the role of phenylpropanoid and vitamin biosynthesis, and it will provide a basis for future investigation and application in the blanch cultivation of water dropwort.

Isolation and Identification of Bioactive Compounds from Bidens spp. Using HPLC-DAD and GC-MS Analysis and Their Biological Activity as Anticancer Molecules.

The genus Bidens a member of family Compositae, is widely documented as an ethno-medicinally important genus of plants. In the present study, anticancer potential of three ethno-medicinally important species i.e., B. bipinnata, B. biternata and B. pilosa were tested. For in-vitro evaluation, an MTT (Thiazolyl blue tetrazolium bromide) assay was performed against cervical cancer cells (HeLa), hepatocellular carcinoma (HepG), and adenocarcinoma human alveolar basal epithelial cells (A549). For in vivo evaluation, Artemia salina, Danio rerio, and Caenorhabditis elegans were used. Among all the tested extracts, the ethanol extract of B. biternata appeared to have highest anticancer activity, and the compounds responsible for this activity were identified to be Tris (2,4-di-tert-butylphenyl), 4-hydroxy-2,4'-dimethoxychalcone, and 2,4-di-tert-butylphenol. This is the first report of the isolation of Tris (2,4-di-tert-butylphenyl) phosphate from the genus Bidens and the first report of 4-hydroxy-2,4'-dimethoxychalcone and 2,4-di-tert-butylphenol from B. biternata. Among the isolated compounds, 4-hydroxy-2,4'-dimethoxychalcone showed the highest anticancer activity with an LD50 value of 236.7 µg/mL. Therefore, this compound carries promising potential for being established as a pharmaceutical for chemoprevention and chemotherapy.

Therapeutic prospects of endophytic Bacillus species from Berberis lycium against oxidative stress and microbial pathogens.

Endophytes are microorganisms residing within plant tissues. Bacterial endophytes are important sources for production of pharmaceutically important metabolites. Berberis lycium is an important medicinal plant and there exist no report regarding isolation and determination of bioactive potential of its bacterial endophytes. Therefore the present study was aimed to isolate and identify bacterial endophytes from Berberis lycium. The study resulted in isolation of 20 strains of bacterial endophytes. Based on their antibacterial activity three strains were identified as Bacillus cereus (LBL6), Bacillus thuringiensis (SBL3) and Bacillus anthracis (SBL4) on basis of 16SrRNA gene using universal primers. Crude ethyl acetate extracts of LBL6, SBL3 and SBL4 were further evaluated for antioxidant and antifungal activities. Moderate antioxidant activity (56 %) at a concentration of 1000 µg/mL was observed for LBL6 followed by 45 and 43 % activity by SBL4 and SBL3 respectively. Significant antifungal activity was observed against Aspergillus niger (60 %) and Aspergillus flavus (56 %) at concentration of 4 mg/mL of SBL3 and SBL4 respectively. GCMS analysis of extract (LBL6) exhibited presence of 12 bioactive secondary metabolites corresponding to antimicrobial, antifungal, antioxidant, antitumor and anticancer activities. In conclusion, present study highlighted the importance of Berberis lycium to host diverse bacterial endophytes of pharmaceutical importance.

Identification of Raf-Like Kinases B Subfamily Genes in Gossypium Species Revealed GhRAF42 Enhanced Salt Tolerance in Cotton.

Salinity is a critical abiotic factor that significantly reduces agricultural production. Cotton is an important fiber crop and a pioneer on saline soil, hence genetic architecture that underpins salt tolerance should be thoroughly investigated. The Raf-like kinase B-subfamily (RAF) genes were discovered to regulate the salt stress response in cotton plants. However, understanding the RAFs in cotton, such as Enhanced Disease Resistance 1 and Constitutive Triple Response 1 kinase, remains a mystery. This study obtained 29, 28, 56, and 54 RAF genes from G. arboreum, G. raimondii, G. hirsutum, and G. barbadense, respectively. The RAF gene family described allopolyploidy and hybridization events in allotetraploid cotton evolutionary connections. Ka/Ks analysis advocates that cotton evolution was subjected to an intense purifying selection of the RAF gene family. Interestingly, integrated analysis of synteny and gene collinearity suggested dispersed and segmental duplication events involved in the extension of RAFs in cotton. Transcriptome studies, functional validation, and virus-induced gene silencing on salt treatments revealed that GhRAF42 is engaged in salt tolerance in upland cotton. This research might lead to a better understanding of the role of RAFs in plants and the identification of suitable candidate salt-tolerant genes for cotton breeding.

Combined application of zinc and silicon alleviates terminal drought stress in wheat by triggering morpho-physiological and antioxidants defense mechanisms.

Wheat is an important global staple food crop; however, its productivity is severely hampered by changing climate. Erratic rain patterns cause terminal drought stress, which affect reproductive development and crop yield. This study investigates the potential and zinc (Zn) and silicon (Si) to ameliorate terminal drought stress in wheat and associated mechanisms. Two different drought stress levels, i.e., control [80% water holding capacity (WHC) was maintained] and terminal drought stress (40% WHC maintained from BBCH growth stage 49 to 83) combined with five foliar-applied Zn-Si combinations (i.e., control, water spray, 4 mM Zn, 40 mM Si, 4 mM Zn + 40 mM Si applied 7 days after the initiation of drought stress). Results revealed that application of Zn and Si improved chlorophyll and relative water contents under well-watered conditions and terminal drought stress. Foliar application of Si and Zn had significant effect on antioxidant defense mechanism, proline and soluble protein, which showed that application of Si and Zn ameliorated the effects of terminal drought stress mainly by regulating antioxidant defense mechanism, and production of proline and soluble proteins. Combined application of Zn and Si resulted in the highest improvement in growth and antioxidant defense. The application of Zn and Si improved yield and related traits, both under well-watered conditions and terminal drought stress. The highest yield and related traits were recorded for combined application of Zn and Si. For grain and biological yield differences among sole and combined Zn-Si application were statistically non-significant (p>0.05). In conclusion, combined application of Zn-Si ameliorated the adverse effects of terminal drought stress by improving yield through regulating antioxidant mechanism and production of proline and soluble proteins. Results provide valuable insights for further cross talk between Zn-Si regulatory pathways to enhance grain biofortification.

Analysis of Peganum harmala, Melia azedarach and Morus alba extracts against six lethal human cancer cells and oxidative stress along with chemical characterization through advance Fourier Transform and Nuclear Magnetic Resonance spectroscopic methods towards green chemotherapeutic agents.

Traditional medicines implicate consumption of plant crude extracts, which may consist of extensive phytochemical diversity. Overall, the most biologically active extract of Peganum harmala (seeds) exhibited significant cytotoxic activity on Artemia salina with LC50 value of 61.547 µg/mL, while P. harmala (roots) [LC50 = 124.229 µg/mL] and M. azedarach (fruits) [LC50 = 147.813 µg/mL] showed moderate cytotoxic potential. P. harmala (seeds) extract also showed the maximum antitumor potential with 52.278 µg/mL LC50. Branches of P. harmala and Morus alba were not active in both bioassays. These outcomes were further reinforced by the levels of phenolics and flavonoids checked against gallic acid and quercetin equivalents, respectively, by standard curves. Current study aims to isolate, structurally characterize and analyze the bioactive compound from plant extracts by using chromatographic and spectrophotometric techniques. Bioactivity guided isolation of extracts led to the isolation of PH-HM-16 from ethyl acetate fraction P. harmala seeds. Chemical structure of PH-HM-16 was elucidated by ESI-MS, 1H NMR, 13C NMR, HSQC and IR spectrum. The results demonstrated significant positive anticancer activities against six human cancer cell lines assessed through MTT cancer cell growth inhibition assay. PH-HM-16 was most effective against prostate cancer cell lines [IC50 = 17.63 µg/mL] followed by breast cancer cell line MCF7 [IC50 value of 41.81 µg/mL]. IC50 value of PH-HM-16 against human myeloid leukemia cell line HL-60 and human colorectal tumor cells HCT-116 was observed as 68.77 µg/mL and 71.54 µg/mL respectively. The IC 50 value of PH-HM-16 compound was not significant against human gastric cancer SGC-7901 (111.89 µg/mL) and human lung adenocarcinoma epithelial cell line A549 (176.04 µg/mL). Isolated bioactive metabolite PH-HM-16 possesses significant antitumor potential so this could be the first step to develop an effective anticancer agent. Hence, this compound represents a promising potential to be chemically standardized or developed into pharmaceuticals for the chemoprevention and/or the treatment of certain types of cancer, especially as adjuvant phytotherapeutics in conventional chemotherapy.

Genome wide analysis of IQD gene family in diploid and tetraploid species of cotton (Gossypium spp.).

Calmodulin (CaM) is considered as the most significant Ca2+ signaling messenger that mediate various biochemical and physiological reactions. IQ domain (IQD) proteins are plant specific CML/CaM calcium binding which are characterized by domains of 67 amino acids. 50, 50, 94, and 99 IQD genes were detected from G. arboreum (A2), G. raimondii (D5), G. barbadense (AD2) and G. hirsutum (AD1) respectively. Existence of more orthologous genes in cotton species than Arabidopsis, advocated that polyploidization produced new cotton specific orthologous gene clusters. Duplication of gene events depicts that IQD gene family of cotton evolution was under strong purifying selection. G. hirsutum exhibited high level synteny. GarIQD25 exhibited high expression in stem, root, flower, ovule and fiber in G. arboreum. In G. raimondii, GraIQD03 demonstrated upregulation across stem, ovule, fiber and seed. GbaIQD11 and GbaIQD62 exhibited upregulation in fiber development in G. barbadense. GhiIQD69 recognized as main candidate genes for plant parts, floral tissues, fiber and ovule development. Promotor analysis identified cis-regulatory elements were involved in plant growth and development. Overwhelmingly, present study paves the way to better understand the evolution of cotton IQD genes and lays a foundation for future investigation of IQD in cotton.

Genome wide identification, classification and functional characterization of heat shock transcription factors in cultivated and ancestral cottons (Gossypium spp.).

Heat shock transcription factors (HSF) have been demonstrated to play a significant transcriptional regulatory role in plants and considered as an integral part of signal transduction pathways against environmental stresses especially heat stress. Despite of their importance, HSFs have not yet been identified and characterized in all cotton species. In this study, we report the identification of 42, 39, 67, and 79 non-redundant HSF genes from diploid cottons G. arboreum (A2) and G. raimondii (D5), and tetraploid cottons G. barbadense (AD2) and G. hirsutum (AD1) respectively. The chromosome localization of identified HSFs revealed their random distribution on all the 13 chromosomes of A and D genomes of cotton with few regions containing HSFs in clusters. The genes structure and conserved domain analysis revealed the family-specific conservation of intron/exon organization and conserved domains in HSFs. Various abiotic stress-related cis-regulatory elements were identified from the putative promoter regions of cotton HSFs suggesting their possible role in mediating abiotic stress tolerance. The combined phylogenetic analysis of all the cotton HSFs grouped them into three subfamilies; with 145 HSFs belong to class A, 85 to class B, and 17 to class C subfamily. Moreover, a detailed analysis of HSF gene family in four species of cotton elucidated the role of allopolyploid and hybridization during evolutionary cascade of allotetraploid cotton. Comparatively, existence of more orthologous genes in cotton species than Arabidopsis, advocated that polyploidization produced new cotton specific orthologous gene clusters. Phylogenetic, collinearity and multiple synteny analyses exhibited dispersed, segmental, proximal, and tandem gene duplication events in HSF gene family. Duplication of gene events suggests that HSF gene family of cotton evolution was under strong purifying selection. Expression analysis revealed that GarHSF04 were found to be actively involved in PEG and salinity tolerance in G. arboreum. GhiHSF14 upregulated in heat and downregulated in salinity whilst almost illustrated similar behavior under cold and PEG treatments and GhiHSF21 exhibited down regulation almost across all the stresses in G. hirsutum. Overwhelmingly, present study paves the way to better understand the evolution of cotton HSF TFs and lays a foundation for future investigation of HSFs in improving abiotic stress tolerance in cotton.

Social Network Analysis of an Online Smoking Cessation Community to Identify Users' Smoking Status.

OBJECTIVES: Users share valuable information through online smoking cessation communities (OSCCs), which help people maintain and improve smoking cessation behavior. Although OSCC utilization is common among smokers, limitations exist in identifying the smoking status of OSCC users ("quit" vs. "not quit"). Thus, the current study implicitly analyzed user-generated content (UGC) to identify individual users' smoking status through advanced computational methods and real data from an OSCC. METHODS: Secondary data analysis was conducted using data from 3,833 users of BcomeAnEX.org. Domain experts reviewed posts and comments to determine the authors' smoking status when they wrote them. Seven types of feature sets were extracted from UGC (textual, Doc2Vec, social influence, domain-specific, author-based, and thread-based features, as well as adjacent posts). RESULTS: Introducing novel features boosted smoking status recognition (quit vs. not quit) by 9.3% relative to the use of text-only post features. Furthermore, advanced computational methods outperformed baseline algorithms across all models and increased the smoking status prediction performance by up to 12%. CONCLUSIONS: The results of this study suggest that the current research method provides a valuable platform for researchers involved in online cessation interventions and furnishes a framework for on-going machine learning applications. The results may help practitioners design a sustainable real-time intervention via personalized post recommendations in OSCCs. A major limitation is that only users' smoking status was detected. Future research might involve programming machine learning classification methods to identify abstinence duration using larger datasets.