Control of adventitious rooting in the alpine perennial Arabia alpina

Successful adventitious root development ensures the efficient clonal propagation of alpine perennials under harsh environmental conditions, but the molecular basis of this process is not well understood. I used the alpine perennial Arabis alpina to explore natural variation of adventitious rooting and investigate the molecular basis of adventitious root development in alpine perennials. Plants of the A. alpina accessions, Pajares (Paj), Dorfertal (Dor), Totes Gebirge (Tot) and West Carpathians (Wca), and the perpetual flowering 1-1 (pep1-1) mutant were scored after growth in a long day greenhouse. The occupancy of adventitious roots on the hypocotyl, main stem, and axillary branches varied between genotypes. Especially, Wca plants produced adventitious roots on the main stem, which correlated with the higher expression of the A. alpina homolog of GH3.3. Exogenous auxin application by foliar spraying promoted adventitious root formation robustly in a genotype and age-dependent manner. I also applied auxin spray on vernalized Paj plants and scored the presence of adventitious roots on stems after plants were transferred in long day greenhouse. Adventitious roots developed from the vascular cambium cells specifically on younger internodes. High-throughput RNA sequencing revealed the differential regulation of auxin transporter genes in the internodes that produce adventitious roots compared to the ones that do not, indicating a key role for polar auxin transport during the induction of adventitious rooting after auxin spray. Auxin-responsive genes showed internode-specific transcript accumulation in response to auxin spray, which correlated with their rooting ability. In addition, transcripts of several meristem-associated genes were enhanced in the internodes that develop adventitious roots after auxin spray, indicating the establishment of root primordium during vernalization. Extended vernalization overcame the requirement to spray with synthetic auxin for the development of adventitious roots. After 21 weeks of vernalization, adventitious roots developed in young internodes and transcriptome profiling indicated the presence of initiator cells during vernalization and the involvement of auxin during the establishment of the initiator cells

Genetic Basis of Flocculation in Azospirillum brasilense.

Azospirillum brasilense is a class of rhizobacteria capable of nitrogen fixation, root colonization and hence promoting host plant growth. The bacteria posses cell interaction behaviors like clumping and flocculation that contribute the survival of the organism in nutrient limited conditions. Change in the cell surface adhesive properties allows the cells to progress from free swimming to clumping and finally flocculation. Less is known about the genetic regulation of these processes with flcA being the only transcriptional regulator known so far to directly control flocculation. Recent evidence suggesting that Che1, a chemotaxis like signal transduction pathway controls the cell behavior clumping and hence indirectly controlling flocculation, To understand the genetic regulation of clumping and flocculation in A. brasilense Sp7, the research here focuses on a subset of 27 of these transposon mutants. The objective of this research was to map the insertion by rescue cloning, characterize the mutant for growth, motility and clumping using qualitative and quantitative assays and characterize the effect of the mutations identified on flocculation. By rescue cloning we mapped the transposon insertion for nine out of the twenty-seven mutations. The insertions were mapped on glycosyltransferases, glucosyltransferase, putative TonB-dependent siderophore receptor, Acyl-CoA thioesterase and sugar phosphatase of the HAD superfamily. All these mutants were characterized for the clumping and flocculation phenotype and while some of these mutants showed severe delay in clumping and/or flocculation indicating changes in cell surface adhesive properties of the mutants compared to the wild type. Further investigations like supplementation of media with different sources of iron provided an insight into understanding the indirect effect of the PM3 mutation on the iron transport pathway. Also, the lectin-binding assay provided insightful information about the changes in the exopolysaccharide (EPS) composition during the different stages of cell aggregation. In conclusion the experiments provided good measure of information about the changes in the cell surface properties specifically with relation to proteins and EPS as an initial investigation. Future work will concentrate on screening the effects of these mutations on the expression of downstream genes and further phenotypic characterization of the selected mutants

MalDicom: A Memory Forensic Framework for Detecting Malicious Payload in DICOM Files

Digital Imaging and Communication System (DICOM) is widely used throughout the public health sector for portability in medical imaging. However, these DICOM files have vulnerabilities present in the preamble section. Successful exploitation of these vulnerabilities can allow attackers to embed executable codes in the 128-Byte preamble of DICOM files. Embedding the malicious executable will not interfere with the readability or functionality of DICOM imagery. However, it will affect the underline system silently upon viewing these files. This paper shows the infiltration of Windows malware executables into DICOM files. On viewing the files, the malicious DICOM will get executed and eventually infect the entire hospital network through the radiologist's workstation. The code injection process of executing malware in DICOM files affects the hospital networks and workstations' memory. Memory forensics for the infected radiologist's workstation is crucial as it can detect which malware disrupts the hospital environment, and future detection methods can be deployed. In this paper, we consider the machine learning (ML) algorithms to conduct memory forensics on three memory dump categories: Trojan, Spyware, and Ransomware, taken from the CIC-MalMem-2022 dataset. We obtain the highest accuracy of 75% with the Random Forest model. For estimating the feature importance for ML model prediction, we leveraged the concept of Shapley values

Super-resolution and super-sensitivity of quantum LiDAR with multi-photonic state and binary outcome photon counting measurement

Here we are investigating the enhancement in phase sensitivity and resolution in Mach-Zehnder interferometer (MZI) based quantum LiDAR. We are using multi-photonic state (MPS), superposition of four coherent states [1], as the input state and binary outcome parity photon counting measurement and binary outcome zero-nonzero photon counting measurement as the measurement schemes. We thoroughly investigate the results in lossless as well as in lossy cases. We found enhancement in resolution and phase sensitivity in comparison to the coherent state and even coherent superposition state (ECSS) based quantum LiDAR. Our analysis shows that MPS may be an alternative nonclassical resource in the field of quantum imaging and quantum sensing technologies, like in quantum LiDAR.Comment: We welcome comment

A pilot study to evaluate the effectiveness of adjunctive use of two antimicrobial topical gels in chronic gingivitis

Gingivitis is one of the most prevalent oral disease in humans. The most important etiological factor of gingivitis is dental plaque. Plaque control procedures comprises of several mechanical and chemical methods. Many studies have advocated that chemica

MediHunt: A Network Forensics Framework for Medical IoT Devices

The Medical Internet of Things (MIoT) has enabled small, ubiquitous medical devices to communicate with each other to facilitate interconnected healthcare delivery. These devices interact using communication protocols like MQTT, Bluetooth, and Wi-Fi. However, as MIoT devices proliferate, these networked devices are vulnerable to cyber-attacks. This paper focuses on the vulnerabilities present in the Message Queuing Telemetry and Transport (MQTT) protocol. The MQTT protocol is prone to cyber-attacks that can harm the system's functionality. The memory-constrained MIoT devices enforce a limitation on storing all data logs that are required for comprehensive network forensics. This paper solves the data log availability challenge by detecting the attack in real-time and storing the corresponding logs for further analysis with the proposed network forensics framework: MediHunt. Machine learning (ML) techniques are the most real safeguard against cyber-attacks. However, these models require a specific dataset that covers diverse attacks on the MQTT-based IoT system for training. The currently available datasets do not encompass a variety of applications and TCP layer attacks. To address this issue, we leveraged the usage of a flow-based dataset containing flow data for TCP/IP layer and application layer attacks. Six different ML models are trained with the generated dataset to evaluate the effectiveness of the MediHunt framework in detecting real-time attacks. F1 scores and detection accuracy exceeded 0.99 for the proposed MediHunt framework with our custom dataset