Task Scheduling Approach in Cloud Computing Environment Using Hybrid Differential Evolution

Task scheduling is one of the most significant challenges in the cloud computing environment and has attracted the attention of various researchers over the last decades, in order to achieve cost-effective execution and improve resource utilization. The challenge of task scheduling is categorized as a nondeterministic polynomial time (NP)-hard problem, which cannot be tackled with the classical methods, due to their inability to find a near-optimal solution within a reasonable time. Therefore, metaheuristic algorithms have recently been employed to overcome this problem, but these algorithms still suffer from falling into a local minima and from a low convergence speed. Therefore, in this study, a new task scheduler, known as hybrid differential evolution (HDE), is presented as a solution to the challenge of task scheduling in the cloud computing environment. This scheduler is based on two proposed enhancements to the traditional differential evolution. The first improvement is based on improving the scaling factor, to include numerical values generated dynamically and based on the current iteration, in order to improve both the exploration and exploitation operators; the second improvement is intended to improve the exploitation operator of the classical DE, in order to achieve better results in fewer iterations. Multiple tests utilizing randomly generated datasets and the CloudSim simulator were conducted, to demonstrate the efficacy of HDE. In addition, HDE was compared to a variety of heuristic and metaheuristic algorithms, including the slime mold algorithm (SMA), equilibrium optimizer (EO), sine cosine algorithm (SCA), whale optimization algorithm (WOA), grey wolf optimizer (GWO), classical DE, first come first served (FCFS), round robin (RR) algorithm, and shortest job first (SJF) scheduler. During trials, makespan and total execution time values were acquired for various task sizes, ranging from 100 to 3000. Compared to the other metaheuristic and heuristic algorithms considered, the results of the studies indicated that HDE generated superior outcomes. Consequently, HDE was found to be the most efficient metaheuristic scheduling algorithm among the numerous methods researched

The Glucocorticoid Receptor and Certain KRÜPPEL-Like Transcription Factors have the Potential to Synergistically Stimulate Bovine Herpesvirus 1 Transcription and Reactivation from Latency

Bovine herpesvirus 1 (BoHV-1), an important bovine pathogen, establishes life-long latency in sensory neurons within trigeminal ganglia (TG). Stress, as mimicked by the synthetic corticosteroid dexamethasone, consistently induces reactivation in calves latently infected with BoHV-1. Dexamethasone induces expression of several transcription factors in TG neurons during early stages of reactivation, including Krüppel-like transcription factors (KLF): KLF4, KLF6, KLF15, and promyelocytic leukemia zinc finger. Furthermore, the glucocorticoid receptor (GR) is consistently detected in TG neurons expressing viral regulatory proteins during reactivation from latency. The viral immediate early transcription unit 1 (IEtu1) promoter that drives expression of two viral transcription factors (bICP0 and bICP4) contains two GR response elements (GREs) and is stimulated by DEX. KLF15 and the GR form a feed forward transcription loop that synergistically stimulates productive infection and IEtu1 promoter activity. New studies demonstrate the GR and KLF6 synergistically stimulate productive infection and IEtu1 promoter activity if the GREs are intact. Furthermore, the GR and KLF6 interact with wild-type GREs within the IEtu1 promoter, but not GRE mutants. These studies suggest that certain KLF family members and the GR can convert a silent viral genome in latently infected neurons into an actively transcribing genome during reactivation from latency

Prospective study for commercial and low-cost hyperspectral imaging systems to evaluate thermal tissue effect on bovine liver samples

Thermal ablation modalities, for example radiofrequency ablation (RFA) and microwave ablation, are intended to prompt controlled tumour removal by raising tissue temperature. However, monitoring the size of the resulting tissue damage during the thermal removal procedures is a challenging task. The objective of this study was to evaluate the observation of RFA on an ex vivo liver sample with both a commercial and a low-cost system to distinguish between the normal and the ablated regions as well as the thermally affected regions. RFA trials were conducted on five different ex vivo normal bovine samples and monitored initially by a custom hyperspectral (HS) camera to measure the diffuse reflectance (Rd) utilising a polychromatic light source (tungsten halogen lamp) within the spectral range 348–950 nm. Next, the light source was replaced with monochromatic LEDs (415, 565 and 660 nm) and a commercial charge-coupled device (CCD) camera was used instead of the HS camera. The system algorithm comprises image enhancement (normalisation and moving average filter) and image segmentation with K-means clustering, combining spectral and spatial information to assess the variable responses to polychromatic light and monochromatic LEDs to highlight the differences in the Rd properties of thermally affected/normal tissue regions. The measured spectral signatures of the various regions, besides the calculation of the standard deviations (δ) between the generated six groups, guided us to select three optimal wavelengths (420, 540 and 660 nm) to discriminate between these various regions. Next, we selected six spectral images to apply the image processing to (at 450, 500, 550, 600, 650 and 700 nm). We noticed that the optimum image is the superimposed spectral images at 550, 600, 650 and 700 nm, which are capable of discriminating between the various regions. Later, we measured Rd with the CCD camera and commercially available monochromatic LED light sources at 415, 565 and 660 nm. Compared to the HS camera results, this system was more capable of identifying the ablated and the thermally affected regions of surface RFA than the side-penetration RFA of the investigated ex vivo liver samples. However, we succeeded in developing a low-cost system that provides satisfactory information to highlight the ablated and thermally affected region to improve the outcome of surgical tumour ablation with much shorter time for image capture and processing compared to the HS system

A new low molecular mass alkaline cyclodextrin glucanotransferase from Amphibacillus sp. NRC-WN isolated from an Egyptian soda lake

Background: Cyclodextrin glucanotransferase (CGTase) is one of the most industrially important enzymes used in the commercial production of cyclodextrins (CDs). Alkaliphilic bacteria have attracted much interest in the last few decades because of their ability to produce extracellular enzymes that are active and stable at high pH values. Here, we report the isolation of a new CGTase from alkaliphilic bacteria collected from Egyptian soda lakes and describe the purification and biochemical characterization of this CGTase. Results: Screening for CGTase-producing alkaliphilic bacteria from sediment and water samples collected from Egyptian soda lakes located in the Wadi Natrun valley resulted in the isolation of a potent CGTase-producing alkaliphilic bacterial strain, designated NRC-WN. Strain NRC-WN was belonging to genus Amplibacullus by 16S rDNA sequence analysis (similarity: ca. 98%). Among the tested nitrogen and carbon sources, peptone (0.15%, w/v) and soluble starch (0.4%, w/v) allowed maximal CGTase production by Amphibacillus sp. NRC-WN. CGTase was successfully purified from Amphibacillus sp. NRC-WN up to 159.7-fold through a combination of starch adsorption and anion exchange chromatography, resulting in a yield of 84.7%. SDS-PAGE analysis indicated that the enzyme was purified to homogeneity and revealed an estimated molecular mass of 36 kDa, which makes it one of the smallest CGTases reported in the literature. The purified enzyme exhibited maximum activity at 50oC and was stable up to 70oC, retaining 93% of its initial activity after treatment for 1 hr. Furthermore, Ca2+ ions (10 mM) significantly enhanced the thermal stability of the CGTase. The purified enzyme was active and stable over a wide pH range, showing maximal activity at pH 9.5. The enzyme was significantly stimulated by Zn2+, Ca2+ and Co2+ but was completely inhibited in the presence of Fe3+ and mercaptoethanol. The Km and Vmax values of the purified CGTase were estimated to be 0.0434 mg/ml and 3,333.3 mg \u3b2-CD/ml/min, respectively. \u3b2-CD was the predominant product of starch degradation by the Amphibacillus sp. NRC-WN CGTase, followed by \u3b1-and \u3b3-CDs. Conclusions: A new low molecular mass alkaline CGTase was purified from a newly identified alkaliphilic Amphibacillus sp. NRC-WN isolate from the Egyptian soda lakes. The enzyme showed promising thermal and pH stability and a high affinity toward starch as a natural substrate

Multiple Brain Abscesses Complicating Enterobacter Cloacae Sepsis in a Preterm Neonate with Atypical MRI Appearance: A Case Report

Multiple brain abscesses in neonates are extremely rare and occur as an unusual complication of bacterial meningitis or sepsis. There are many bacterial pathogens reported to cause brain abscesses in newborns; however, brain abscess caused by Enterobacter cloacae has rarely been described previously in neonates and only a few reports exist in English literature. After 4 weeks in the neonatal intensive care unit, a preterm neonate developed multiple brain abscesses as a complication of E. cloacae sepsis which were revealed on TFU and confirmed on MRI. Consequently, a limited craniotomy was performed for the biggest abscess for evacuation of pus and followed by aspiration of other abscesses

Three Ways to Protect Against Ransomware

Our project proposes a new solution that provides three layers of protection against ransomware. Ransomware infects computer systems by tricking a user into clicking on a link, then preventing access to that machine until the user pays a ransom. Our first defense layer provides the user with mechanisms to prevent them from clicking on malicious links. This is achieved by using outlook add-ins which disable hyperlinks and provide additional information to help users make the right decision. Our second defense layer uses machine learning algorithms to learn about normal user behavior, then capture an abnormal event which could potentially harm the user\u27s machine to stop a ransomware infection. Abnormal behavior could include a process that encrypts the whole hard disk drive. Our third defense layer provides the system admin with a way of being notified by computer infections, capturing this data and providing ways of locking down the user machine to prevent further damage. The information is saved in real-time in a database in the cloud in order to help minimize risk. From here, the admin is then able to take immediate action if an infection occurs