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.

GhiPLATZ17 and GhiPLATZ22, zinc-dependent DNA-binding transcription factors, promote salt tolerance in upland cotton.

KEY MESSAGE: The utilization of transcriptome analysis, functional validation, VIGS, and DAB techniques have provided evidence that GhiPLATZ17 and GhiPLATZ22 play a pivotal role in improving the salt tolerance of upland cotton. PLATZ (Plant AT-rich sequences and zinc-binding proteins) are known to be key regulators in plant growth, development, and response to salt stress. In this study, we comprehensively analyzed the PLATZ family in ten cotton species in response to salinity stress. Gossypium herbaceum boasts 25 distinct PLATZ genes, paralleled by 24 in G. raimondii, 25 in G. arboreum, 46 in G. hirsutum, 48 in G. barbadense, 43 in G. tomentosum, 67 in G. mustelinum, 60 in G. darwinii, 46 in G. ekmanianum, and a total of 53 PLATZ genes attributed to G. stephensii. The PLATZ gene family shed light on the hybridization and allopolyploidy events that occurred during the evolutionary history of allotetraploid cotton. Ka/Ks analysis suggested that the PLATZ gene family underwent intense purifying selection during cotton evolution. Analysis of synteny and gene collinearity revealed a complex pattern of segmental and dispersed duplication events to expand PLATZ genes in cotton. Cis-acting elements and gene expressions revealed that GhiPLATZ exhibited salt stress resistance. Transcriptome analysis, functional validation, virus-induced gene silencing (VIGS), and diaminobenzidine staining (DAB) demonstrated that GhiPLATZ17 and GhiPLATZ22 enhance salt tolerance in upland cotton. The study can potentially advance our understanding of identifying salt-resistant genes in cotton.

Detection of haptoglobin and serum amyloid A as biomarkers in naturally infected Mycoplasma bovis calves.

The livestock sector of Pakistan is increasing rapidly and it plays important role both for rural community and national economy. It is estimated that almost 8 million rural people are involved in livestock rearing and earning about 35-40 % of their income from the livestock sector. Mycoplasma bovis (M. bovis) infection causes significant economic losses in dairy animals especially young calf in the form of clinical illnesses such as pneumonia, poly-arthritis, respiratory distress and mortality. M. bovis is hard to diagnose and control because of uneven disease appearance and it is usually noticed in asymptomatic animals. For the identification of M. bovis in sub-clinical and clinical samples, determination of acute phase proteins i.e., haptoglobin (Hp) and serum amyloid A (SAA) are important tools for the timely diagnosis of disease. Therefore, early diagnosis of disease and hemato-biochemical changes are considered beneficial tools to control the infectious agent to uplift the economy of the dairy farmers. For this purpose, blood samples were collected from 200 calves of Bovidae family. Serum was separated from blood samples to determine the concentration of Hp and SAA, while blood samples were processed to determine hematological changes in blood from calves by using hematological analyzer. The blood plasma obtained from the blood samples was processed to measure oxidative stress factors. Lungs tissues from slaughterhouses/ morbid calves were collected to observe histopathological changes. The results of present study indicated that level of SAA and Hp remarkably increased (P < 0.05) in M. bovis infected calves in comparison to healthy calves. The oxidative stress markers indicated that nitric oxide and MDA levels in the infected calves increased significantly (P < 0.05), while infected claves had considerably lower levels of superoxide dismutase, catalase and glutathione. These findings indicate that oxidative stress play role to increase the level of APPs, while monitoring of APPs levels may serve as a valuable addition to the clinical evaluation of naturally infected calves with M. bovis. The hematological parameters were decreased significantly (P < 0.05). Altogether, this study suggests that Hp and SAA are proposed as promising biomarkers for detecting naturally occurring M. bovis infection in calves.

Results of the 2023 ISBI challenge to reduce GABA-edited MRS acquisition time.

PURPOSE: Use a conference challenge format to compare machine learning-based gamma-aminobutyric acid (GABA)-edited magnetic resonance spectroscopy (MRS) reconstruction models using one-quarter of the transients typically acquired during a complete scan. METHODS: There were three tracks: Track 1: simulated data, Track 2: identical acquisition parameters with in vivo data, and Track 3: different acquisition parameters with in vivo data. The mean squared error, signal-to-noise ratio, linewidth, and a proposed shape score metric were used to quantify model performance. Challenge organizers provided open access to a baseline model, simulated noise-free data, guides for adding synthetic noise, and in vivo data. RESULTS: Three submissions were compared. A covariance matrix convolutional neural network model was most successful for Track 1. A vision transformer model operating on a spectrogram data representation was most successful for Tracks 2 and 3. Deep learning (DL) reconstructions with 80 transients achieved equivalent or better SNR, linewidth and fit error compared to conventional 320 transient reconstructions. However, some DL models optimized linewidth and SNR without actually improving overall spectral quality, indicating a need for more robust metrics. CONCLUSION: DL-based reconstruction pipelines have the promise to reduce the number of transients required for GABA-edited MRS.

Selective (sono)photocatalytic cleavage of lignin-inspired ß-O-4 linkages to phenolics by ultrasound derived 1-D titania nanomaterials.

Catalytic conversion of lignin to value-added aromatic compounds is still an open challenge, since the selective cleavage of the linkages interconnecting the aromatic molecules, especially the ß-O-4 ones, is not efficiently achieved yet. Herein, novel titania-based nanostructured materials were synthesized using low-power-low-frequency ultrasound that demonstrated high efficiency for the selective cleavage of Cα-Cß bond of ß-O-4 linkages of lignin-inspired model compounds. Going a step ahead, experiments of sonophotocatalytic valorization of 2-phenoxy-1-phenylethanol were contacted for the first time, where the exposure to ultrasound leading to better conversion and selectivity towards the desired products in the case of the novel ultrasound-synthesized nano-photocatalyst. Mechanistic insights showcased that photogenerated holes are the main active species in the catalytic process. In general, this research work provides a green, effective, and cost-effective approach for the selective and efficient catalytic lignin valorization.

Molecular identification of different toxinogenic strains of Clostridium perfringens and histo-pathological observations of camels died of per-acute entero-toxaemia.

Enterotoxaemia is a severe disease caused by Clostridium perfringens and render high mortality and huge economic losses in livestock. However, scanty information and only few cases are reported about the presence and patho-physiology of enterotoxaemia in camels. The bacterium induces per-acute death in animals due to rapid production of different lethal toxins. The necropsy of camels (per-acute = 15, acute = 3) was conducted at 18 outbreaks of enterotoxaemia in camels in the desert area of Bahawalpur region. At necropsy, the serosal surfaces of visceral organs in the abdominal, peritoneal and thoracic cavities were found to have petechiation with severe congestion. Moreover, both the cut-sections of different visceral organs and the histo-pathological analysis revealed the pathological lesions in heart, lungs, kidneys, spleen, small and large intestines. Grossly, the kidneys were severely congested, hyperemic, swollen and softer in consistency. Under the microscope, different sections of kidneys indicated that the convulated and straight tubules were studded with erythrocytes. In the intestines, there were stunting fusion of crypts and villi. Similarly, various histo-pathological ailments were also observed in the heart, lungs and spleen. At blood agar, the collected samples showed beta hemolytic colonies of C. perfringens that appeared as medium sized rods microscopically and stained positively on Gram staining. Multiplex PCR revealed C. perfringens type A (α and ß2 genes) and D (epsilon gene) and the deaths were found to be significantly higher due to C. perfringens type D compared to those by C. perfringens type A. Hence, it has been concluded that enterotoxaemia in camel affects multiple organs and becomes fatal, if occurred due to C. perfringens type D.

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.

AIROGS: Artificial Intelligence for Robust Glaucoma Screening Challenge.

The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models for glaucoma screening from CFPs have shown promising results in laboratory settings, their performance decreases significantly in real-world scenarios due to the presence of out-of-distribution and low-quality images. To address this issue, we propose the Artificial Intelligence for Robust Glaucoma Screening (AIROGS) challenge. This challenge includes a large dataset of around 113,000 images from about 60,000 patients and 500 different screening centers, and encourages the development of algorithms that are robust to ungradable and unexpected input data. We evaluated solutions from 14 teams in this paper and found that the best teams performed similarly to a set of 20 expert ophthalmologists and optometrists. The highest-scoring team achieved an area under the receiver operating characteristic curve of 0.99 (95% CI: 0.98-0.99) for detecting ungradable images on-the-fly. Additionally, many of the algorithms showed robust performance when tested on three other publicly available datasets. These results demonstrate the feasibility of robust AI-enabled glaucoma screening.

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.

Laser energy partitioning in nanosecond pulsed laser-induced air breakdown: effect of incident laser energy.

Air breakdown is generated by a 1064 nm nanosecond pulsed laser beam, and laser energy deposited in the breakdown (E d), transmitted through the plasma region (E t) and carried away by the shock wave (E s) is estimated for the incident laser energy (E i) range of 60-273 mJ. The E d is approximately 85% of E i at 60 mJ, rapidly increasing to 92% at 102 mJ. The shock wave front velocity and radius are measured as a function of E i and propagation distance. The shock wave velocity nicely follows the v∝E i0.3 trend predicted by the laser-supported detonation wave model. The Sedov-Taylor theory is used to estimate E s, which rapidly increases with E i, but E i to E s conversion linearly decreases from 83% to 48%. At lower values of E i, most of the laser energy is carried away by the shock wave, whereas the laser energy used in plasma heating or released in the form of electromagnetic and thermal radiation becomes important at higher laser energies. This implies that laser energy partitioning is highly dependent on the value of incident laser energy. These findings provide important insights into the fundamental physics of air breakdown and will be useful in a variety of applications such as laser-induced breakdown spectroscopy, laser ignition, and laser propulsion.

Multi-site, Multi-domain Airway Tree Modeling.

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to the quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and extensive clinical efforts for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Both quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage (https://atm22.grand-challenge.org/).

Fetal brain tissue annotation and segmentation challenge results.

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.

High-Density Electroencephalography and Speech Signal Based Deep Framework for Clinical Depression Diagnosis.

Depression is a mental disorder characterized by persistent depressed mood or loss of interest in performing activities, causing significant impairment in daily routine. Possible causes include psychological, biological, and social sources of distress. Clinical depression is the more-severe form of depression, also known as major depression or major depressive disorder. Recently, electroencephalography and speech signals have been used for early diagnosis of depression; however, they focus on moderate or severe depression. We have combined audio spectrogram and multiple frequencies of EEG signals to improve diagnostic performance. To do so, we have fused different levels of speech and EEG features to generate descriptive features and applied vision transformers and various pre-trained networks on the speech and EEG spectrum. We have conducted extensive experiments on Multimodal Open Dataset for Mental-disorder Analysis (MODMA) dataset, which showed significant improvement in performance in depression diagnosis (0.972, 0.973 and 0.973 precision, recall and F1 score respectively) for patients at the mild stage. Besides, we provided a web-based framework using Flask and provided the source code publicly.1.

Transcriptome and proteome profiling revealed the key genes and pathways involved in the fiber quality formation in brown cotton.

Colored cotton is also called eco-cotton because of its natural color fiber. It is inferior in yield and quality than white cotton. The underlying regulatory genes involved in fiber quality and pigment synthesis are not well understood. This study aimed to investigate the transcriptomic and proteomic changes during fiber development in a brown cotton cultivar (Z161) and a white cotton cultivar. The differential proteins with the same expression trend as genes were significantly and positively correlated with corresponding fold changes in expression. Enrichment analysis revealed that Z161, enriched in fiber elongation genes related to flavonoid biosynthesis, phenylalanine metabolism, glutathione metabolism, and many more genes (proteins) are up-regulated. Moreover, 164 glycosyltransferases genes, 15 MYB-bHLH-WD40 genes, and other transcription factors such as C2H2 (12), ERF (11), and NAC (7) were preferentially expressed in Z161. Weighted correlation network analysis identified fatty acid synthesis and energy metabolism as the principal metabolic pathways in both cotton genotypes during fiber development. Identified 15 hub genes will provide important insights for genetic manipulation of fiber quality and pigment deposition balance in brown cotton fibers.

Tuning the physicochemical features of titanium oxide nanomaterials by ultrasound: Elevating photocatalytic selective partial oxidation of lignin-inspired aromatic alcohols.

The research for "green" and economically feasible approaches such as (photo)catalysis especially for biomass valorization such as selective oxidation of biomass derived compounds like aromatic alcohols to corresponding aldehyde by avoiding the harsh reaction conditions and the addition of reagents concentrate the focus of attention the last years. Hence, design and development of novel photocatalyst for the partial selective oxidation is highly desirable. In this research work, ultrasonication of different frequencies (22, 40, 80 kHz) and different amplitudes was utilized as synthesis tool in order to obtain novel materials by precipitation method. The synthesized samples were characterized by using different techniques such as N2 sorption, TEM, XPS, XRD, thermal analysis, and diffuse reflectance spectroscopy. The synthesized sample by using low ultrasound frequency (22 kHz) and amplitude showed a mixed morphological and structural nature consisting of asymmetric 1-dimensional (nanorods-like), layered nano-structures and not well-defined areas, leading to elevate for metal oxide specific surface areas up to 155 m2/g. The observed 1-D nanostructures have diamentions in the range of 20-60 nm. This sample revealed the highest photo-oxidation efficiency for the selective conversion of two biomass-derived, and more specifically lignin-inspired model compounds, benzyl alcohol and cinnamyl alcohol to benzaldehyde and cinnamyl aldehyde, respectively, and hence the highest yield towards the desired aldehydes. The selective photo-oxidation activity was retained even after 5 photocatalytic cycles, while no leaching of Ti was recorded.

Exploring the nano-wonders: unveiling the role of Nanoparticles in enhancing salinity and drought tolerance in plants.

Plants experience diverse abiotic stresses, encompassing low or high temperature, drought, water logging and salinity. The challenge of maintaining worldwide crop cultivation and food sustenance becomes particularly serious due to drought and salinity stress. Sustainable agriculture has significant promise with the use of nano-biotechnology. Nanoparticles (NPs) have evolved into remarkable assets to improve agricultural productivity under the robust climate alteration and increasing drought and salinity stress severity. Drought and salinity stress adversely impact plant development, and physiological and metabolic pathways, leading to disturbances in cell membranes, antioxidant activities, photosynthetic system, and nutrient uptake. NPs protect the membrane and photosynthetic apparatus, enhance photosynthetic efficiency, optimize hormone and phenolic levels, boost nutrient intake and antioxidant activities, and regulate gene expression, thereby strengthening plant's resilience to drought and salinity stress. In this paper, we explored the classification of NPs and their biological effects, nanoparticle absorption, plant toxicity, the relationship between NPs and genetic engineering, their molecular pathways, impact of NPs in salinity and drought stress tolerance because the effects of NPs vary with size, shape, structure, and concentration. We emphasized several areas of research that need to be addressed in future investigations. This comprehensive review will be a valuable resource for upcoming researchers who wish to embrace nanotechnology as an environmentally friendly approach for enhancing drought and salinity tolerance.

Vanadium Stress Alters Sweet Potato (Ipomoea batatas L.) Growth, ROS Accumulation, Antioxidant Defense System, Stomatal Traits, and Vanadium Uptake.

Vanadium (V) is a heavy metal found in trace amounts in many plants and widely distributed in the soil. This study investigated the effects of vanadium concentrations on sweet potato growth, biomass, root morphology, photosynthesis, photosynthetic assimilation, antioxidant defense system, stomatal traits, and V accumulation. Sweet potato plants were grown hydroponically and treated with five levels of V (0, 10, 25, 50, and 75 mg L-1). After 7 days of treatment, V content at low concentration (10 mg L-1) enhanced the plant growth and biomass; in contrast, drastic effects were observed at 25, 50, and 75 mg L-1. Higher V concentrations negatively affect the relative water content, photosynthetic assimilation, photosynthesis, and root growth and reduce tolerance indices. The stomatal traits of sweet potato, such as stomatal length, width, pore length, and pore width, were also decreased under higher V application. Furthermore, V concentration and uptake in the roots were higher than in the shoots. In the same way, reactive oxygen species (ROS) production (hydrogen peroxide), lipid peroxidation (malondialdehyde), osmolytes, glutathione, and enzymes (catalase and superoxide dismutase) activities were increased significantly under V stress. In conclusion, V at a low level (10 mg L-1) enhanced sweet potato growth, and a higher level of V treatment (25, 50, and 75 mg L-1) had a deleterious impact on the growth, physiology, and biochemical mechanisms, as well as stomatal traits of sweet potato.