Process of senescence includes multiple steps involving break-down of chlorophyll to degrade photosynthetic machinery. In this study, we showed that a stress-associated NAC transcription factor ...MpSNAC67 regulates senescence by promoting chlorophyll-catabolic genes. MpSNAC67 encodes a transcriptional activator and its promoter activity is restricted to vascular tissue of banana. Expression of MpSNAC67 showed positive responses to multiple abiotic stress conditions suggesting that MpSNAC67 is a stress associated NAC transcription factor. Transgenic banana lines overexpressing MpSNAC67 showed highly senesced phenotype including yellowing and de-greening of leaves similar to etiolated leaves. Transgenic leaves possessed low chlorophyll content and failed to retain normal chloroplast morphology including loss of granum thylakoid, non-uniform chloroplast membrane and increased number as well as size of plastoglobulins. In a gel shift assay MpSNAC67 could retard the mobility of chlorophyll catabolic genes such as PAO-like (Pheophorbide-a-oxygenase), HCAR-like (hydroxymethyl chlorophyll-a-reductase), NYC/NOL-like (Chlorophyll-b-reductase) as well as ORS1-like (a SenNAC). Expression of these genes were highly elevated in transgenic lines which indicate that MpSNAC67 is a positive regulator of senescence in banana and exercise its effect by regulating the expression of chlorophyll catabolic genes and ORS1.
•Regulation of chlorophyll catabolic genes by banana SNAC67 is demonstrated.•SNAC67 is a stress associated gene with transcriptional activation activity.•Expression of SNAC67 in vascular tissue is regulated through SNBE sites.•SNAC67 induced abnormal chloroplast structure and reduced chlorophyll content.•SNAC67 binds to regulatory region of genes involved in chlorophyll degradation.
Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI). Current methodologies reliant on traditional image ...processing and conventional machine learning encounter hurdles in accurately discerning tumor regions within intricate MRI scans, often susceptible to noise and varying image quality. The advent of artificial intelligence (AI) has revolutionized various aspects of healthcare, providing innovative solutions for diagnostics and treatment strategies. This paper introduces a novel AI-driven methodology for brain tumor detection from MRI images, leveraging the EfficientNetB2 deep learning architecture. Our approach incorporates advanced image preprocessing techniques, including image cropping, equalization, and the application of homomorphic filters, to enhance the quality of MRI data for more accurate tumor detection. The proposed model exhibits substantial performance enhancement by demonstrating validation accuracies of 99.83%, 99.75%, and 99.2% on BD-BrainTumor, Brain-tumor-detection, and Brain-MRI-images-for-brain-tumor-detection datasets respectively, this research holds promise for refined clinical diagnostics and patient care, fostering more accurate and reliable brain tumor identification from MRI images. All data is available on Github: https://github.com/muskan258/Brain-Tumor-Detection-from-MRI-Images-Utilizing-EfficientNetB2 ).
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Breast cancer stands as a paramount public health concern worldwide, underscoring an imperative necessity within the research sphere for precision-driven and efficacious methodologies facilitating ...accurate detection. The existing diagnostic approaches in breast cancer often suffer from limitations in accuracy and efficiency, leading to delayed detection and subsequent challenges in personalized treatment planning. The primary focus of this research is to overcome these shortcomings by harnessing the power of advanced deep learning techniques, thereby revolutionizing the precision and reliability of breast cancer classification. This research addresses the critical need for improved breast cancer diagnostics by introducing a novel Convolutional Neural Network (CNN) model integrated with an Early Stopping callback and ReduceLROnPlateau callback. By enhancing the precision and reliability of breast cancer classification, the study aims to overcome the limitations of existing diagnostic methods, ultimately leading to better patient outcomes and reduced mortality rates. The comprehensive methodology includes diverse datasets, meticulous image preprocessing, robust model training, and validation strategies, emphasizing the model's adaptability and reliability in varied clinical contexts. The findings showcase the CNN model's exceptional performance, achieving a 95.2% accuracy rate in distinguishing cancerous and non-cancerous breast tissue in the integrated dataset, thereby demonstrating its potential for enhancing clinical decision-making and fostering the development of AI-driven diagnostic solutions.
•Characteristics features of fungal effector proteins.•The sequenced genome of biotrophic fungi.•Effector proteins localization.•Strategies of diverse fungal effector proteins.•Current advancements ...in effector characterization.•Managements strategies for biotrophic pathogens.
The interaction of fungal pathogens with their host requires a novel invading mechanism and the presence of various virulence-associated components responsible for promoting the infection. The small secretory proteins, explicitly known as effector proteins, are one of the prime mechanisms of host manipulation utilized by the pathogen to disarm the host. Several effector proteins are known to translocate from fungus to the plant cell for host manipulation. Many fungal effectors have been identified using genomic, transcriptomic, and bioinformatics approaches. Most of the effector proteins are devoid of any conserved signatures, and their prediction based on sequence homology is very challenging, therefore by combining the sequence consensus based upon machine learning features, multiple tools have also been developed for predicting apoplastic and cytoplasmic effectors. Various post-genomics approaches like transcriptomics of virulent isolates have also been utilized for identifying active consortia of effectors. Significant progress has been made in understanding biotrophic effectors; however, most of it is underway due to their complex interaction with host and complicated recognition and signaling networks. This review discusses advances, and challenges in effector identification and highlighted various features of the potential effector proteins and approaches for understanding their genetics and strategies for regulation.
The magnetite embedded mesoporous silica was prepared and functionalized with succinic acid, (abbreviated as Fe-MCM-SUC) for targeted adsorption of uranium from aqueous solution. The adsorbent ...Fe-MCM-SUC was characterized with various advanced spectroscopic and analytical techniques and its adsorption performance towards uranium was evaluated under different conditions of aqueous medium. An apparent uranium adsorption capacity of 430 mg·g
−1
was observed from pH ≥ 6 solution. The adsorbed uranium was recovered easily using dilute Na
2
CO
3
solution and the recovered adsorbent was tested for uranium adsorption in multiple cycles for checking the feasibility of regeneration
.
The results showed that the adsorbent, Fe-MCM-SUC, is a potential candidate for the recovery of uranium from aqueous solution.
The diethylenetriamine (DETA) organic moiety was anchored covalently on the surface of silica gel to obtain a surface-modified adsorbent abbreviated as Si-DETA. The adsorbent was characterized by ...FT-IR, Raman spectroscopy, thermogravimetry, scanning electron microscopy, and energy dispersive X-ray analysis. The adsorption behavior of uranium on Si-DETA was studied as a function of pH of the aqueous phase, duration of contact time, and concentration of uranium in the aqueous phase. The kinetics of uranium adsorption on Si-DETA was fitted with pseudo-first order and pseudo-second order kinetic models. The adsorption isotherm obtained from uranium loading was fitted into popular models such as Langmuir, Freundlich, Temkin, and D-R adsorption isotherms. The statistics of fitting revealed that the Langmuir adsorption model obeyed the adsorption data. The performance of the adsorbent was also evaluated under dynamic conditions by passing feed solution containing uranium in a buffered solution and seawater into a fixed bed column containing Si-DETA. The results were compared with those obtained in a batch mode. The study showed the possibility of using Si-DETA for the separation and recovery of uranium from aqueous waste and seawater.
Immune mechanisms are major players in ensuring the normal functioning of testicular functions. However, apart from their role in active defence against pathogens, prior studies have also suggested a ...possibility for reproduction-related (non-immune) functions of certain immune elements. This study employs a comparative transcriptomics approach followed by network analysis for tracking the variation in the immuno-reproductive milieu of Clarias magur testis in spawning versus pre-spawning phase. The results show a significant modulation of both reproduction and immune-relevant genes in spawning versus pre-spawning phase. The functional enrichment of the upregulated reproduction-relevant gene network also shows immune-related biological processes which indicates a probability of involvement of these candidates in spermatogenesis-related events for switching from pre-spawning to spawning phase. The upregulated immune network is highly dense with 40 hubs, 10 cluster sub-networks and 142 functionally enriched pathways in comparison to its downregulated counterpart with only 5 hubs, 1 cluster and 1 enriched pathway. These findings indicate that the synchronisation in modulation of both reproductive and immune-related factors is critical for progression of testicular events guiding the switch from pre-spawning to spawning phase. The reproductive phase-dependent variation in plasma sex steroid levels and the selected genes for quantitative PCR also corroborated this hypothesis. The study also serves as a preliminary screening step for probable immune candidates that may be involved in reproductive functions of testis in addition to defence.
•Significant modulation of both reproduction and immune-relevant genes in DEG data of spawning versus pre-spawning phase.•Majority representation of pathogen recognition receptor family and their downstream signalling intermediates among the 40 hubs of the upregulated immune-relevant network.•Phase-dependent modulation in the levels of selected immune genes for qPCR profiling across the four reproductive stages shows gradual increase in levels of tlr2, tlr8, tlr13 and il6r from pre-spawning phase to peak levels in post-spawning phase.•Participation of immune elements of testis in non-canonical spermatogenetic processes for progression of the testicular cycle.
Allogeneic hematopoietic SCT is an effective treatment in accelerated (AP) or blast phase (BP) CML. Imatinib (IM) has transient but significant activity in advanced phases of CML, which may permit ...early allografting for responding patients. To identify prognostic factors in allograft recipients previously treated with IM, we analyzed 449 allogeneic hematopoietic SCTs performed from 1999 to 2004 in advanced-phase CML, using the data reported to the Center for International Blood and Marrow Transplant Research. CML patients in second chronic phase (CP2, n=184), AP (n=185) and BP (n=80) received HLA-identical sibling (27%), related (3%), or matched or mismatched unrelated donor (70%), peripheral blood (47%) or BM (53%) hematopoietic SCT after myeloablative (78%) or non-myeloablative (22%) conditioning. In all, 52% in CP2, 49% in AP and 46% in BP received IM before hematopoietic SCT. Disease-free survival was 35-40% for CP2, 26-27% for AP and 8-11% for BP. Cumulative incidence of acute and chronic GVHD and TRM were not affected by the stages of CML or pre-hematopoietic SCT IM exposure. Multivariate analyses showed that conventional prognostic indicators remain the strongest determinants of transplant outcomes. In conclusion, there are no new prognostic indicators of the outcomes of allogeneic hematopoietic SCT for advanced-phase CML in the IM era.
We study here the response of photonic hydrogels (PHs), made of photonic crystals of homogeneous silica particles in polyacrylamide hydrogels (SPHs), to the uranyl ions UO
2
2+
in aqueous solutions. ...It is found that the reflection spectra of the SPH show a peak due to the Bragg diffraction, which exhibits a blue shift in the presence of UO
2
2+
. Upon exposure to the SPH, UO
2
2+
gets adsorbed on the SPH and forms complex coordinate bonds with multiple ligands on the SPH, which causes shrinking of hydrogel and leads to the blue shift in the diffraction peak. The amount of the blue shift in the diffraction peak increases monotonically up to UO
2
2+
concentrations as high as 2300µM. The equilibration time for the shift in the Bragg peak upon exposure to UO
2
2+
is found to be ~30 min. These results are in contrast to the earlier reports on photonic hydrogels of inhomogeneous microgel particles hydrogel (MPH), which shows the threshold UO
2
2+
concentration of ~600 µM, below which the diffraction peak exhibits a blue shift and a change to a red shift above it. The equilibration time for MPH is ~300min. The observed monotonic blue shift and the faster time response of the SPH to UO
2
2+
as compared to the MPH are explained in terms of homogeneous nature of silica particles in the SPH, against the porous and polymeric nature of microgels in the MPH. We also study the extraction of UO
2
2+
from aqueous solutions using the SPH. The extraction capacity estimated by the arsenazo-III analysis is found to be 112 mM/kg.
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