End of Affirmative Action-Who Is Impacted Most? Analysis of Race and Sex Among US Internal Medicine Faculty.

BACKGROUND: For over 50 years, the United States (US) used affirmative action as one strategy to increase diversity in higher education including medical programs, citing benefits including training future public and private sector leaders. However, the recent US Supreme Court ending affirmative action in college admissions threatens advancements in the diversity of medical college faculty. OBJECTIVE: Our study evaluated the demographic trends in Internal Medicine (IM) faculty in the US by assessing sex and race/ethnicity diversity to investigate who is likely to be impacted most with the end of affirmative action. DESIGN: Longitudinal retrospective analysis SUBJECTS: IM faculty from the Association of American Medical Colleges faculty roster from 1966 to 2021 who self-reported sex and ethnicity MAIN OUTCOMES: The primary study measurement was the annual proportion of women and racial/ethnic groups among IM faculty based on academic rank and department chairs. RESULTS: Although racial/ethnic diversity increased throughout the era of affirmative action, African American, Hispanic, and American Indian populations remain underrepresented. White physicians occupied > 50% of faculty positions across academic ranks and department chairs. Among the non-White professors, Asian faculty had the most significant increase in proportion from 1966 to 2021 (0.6 to 16.6%). The percentage of women increased in the ranks of professor, associate professor, assistant professor, and instructor by 19.5%, 27.8%, 25.6%, and 26.9%, respectively. However, the proportion of women and racial/ethnic minority faculty decreased as academic rank increased. CONCLUSION: Despite an increase in the representation of women and racial/ethnic minority IM faculty, there continues to be a predominance of White and men physicians in higher academic ranks. With the end of affirmative action, this trend has the danger of being perpetuated, resulting in decreasing diversity among IM faculty, potentially impacting patient access and health outcomes.

Methamphetamine-induced dental caries: a review of the literature.

Clinical picture of patients taking methamphetamine for long duration includes rampant caries of the smooth surfaces of the whole dentition. The increasing use of methamphetamine in homosexuals is leading to the spread of HIV (human immunodeficiency virus). Easy availability and rapidly spreading nature of this drug (methamphetamine) results in worldwide increase of patients with medical and dental problems. Its effect on human dentition is highly damaging as patients with a beautiful smile begin to present a horrible picture of black, broken, and painful teeth within one year of methamphetamine use. Restoration of aesthetics and function of these teeth is not an easy task, and usually the first step to deal with this condition is counselling the patient to stop using this drug. Knowledge of methamphetamine-induced undesirable effects on the human body is important for the general dental practitioner as referral to mental health services is necessary in this condition.

Identification and primary distribution of Citrus viroid V in citrus in Punjab, Pakistan.

BACKGROUND: Citrus plants are prone to infection by different viroids which deteriorate their vigor and production. Citrus viroid V (CVd-V) is among the six citrus viroids, belongs to genus Apscaviroid (family Pospiviroidae) which induces symptoms of mild necrotic lesions on branches and cracks on trunk portion. METHODS AND RESULTS: A survey was conducted to evaluate the prevalence of CVd-V in core and non-core citrus cultivated areas of Punjab, Pakistan. A total of 154 samples from different citrus cultivars were tested for CVd-V infection by RT-PCR. The results revealed 66.66% disease incidence of CVd-V. Citrus cultivars Palestinia Sweet lime, Roy Ruby, Olinda Valencia, Kaghzi lime, and Dancy were identified as new citrus hosts of CVd-V for the first time from Pakistan. The viroid infection was confirmed by biological indexing on indicator host Etrog citron. The reported primers used for the detection of CVd-V did not amplify, rather showed non-specific amplification, which led to the designing of new primers. Whereas, new back-to-back designed primers (CVd-V AF1/CVd-V AR1) detected CVd-V successfully and obtained an expected amplified product of CVd-V with 294 bp. Sequencing analysis confirmed the new host of CVd-V showing 98-100% nucleotide sequence homology with those reported previously from other countries while 100% sequence homology to the isolates reported from Pakistan. Based on phylogenetic analysis using all CVd-V sequences in GenBank, two main CVd-V groups (I and II) were identified, and newly identified isolates during this study fall in the group I. CONCLUSION: The study revealed that there are some changes in the nucleotide sequences of CVd-V which made difficult for their detection using reported primers. All isolates of Pakistan showed high sequence homology with other isolates of CVd-V from Iran and USA whereas; the isolates from China, Japan, Tunisia, and Africa are distantly related. It is evident that CVd-V is spreading in all citrus cultivars in Pakistan.

First report of Fusarium equiseti causing leaf spots of Bitter gourd (Momordica charantia) in Pakistan.

Bitter gourd (Momordica charantia L.) is an important vegetable crop of the Cucurbitaceae family widely cultivated in Pakistan and around the world. In October 2020, a nutrition management trial of Bitter gourd cv. Seminis-200) was conducted on an area of 10,860 sq. ft. (99×110 feet) at the Agricultural Research farm of Bahauddin Zakariya University, Multan (30.2601° N, 71.5158° E), Pakistan. Symptoms of large, brown necrotic leaf spots were observed on the leaves of bitter gourd vines. The disease started from the yellowing of leaves within the reticulate venation and turned brown. Irregular brown leaf spots coalesced to form large necrotic areas followed by foliar chlorosis then wilting that occurred very late. There were no crown rot symptoms although there was slight discoloration of roots and when cut longitudinally, browning of tissues was observed. The disease was assessed visually with 37% incidence which resulted in poor quality and yield in terms of reduced size and yellowing of fruit. Infected vines along with the roots were collected for the isolation of pathogen. A total of 34 leaves and 22 root samples were collected from the field for isolation. The leaf, collar and root portions were cut into 0.5 to 1 cm in length and surface disinfected with 1% sodium hypochlorite (NaOCl) for 2-3 minutes followed by washing twice with autoclaved distilled water and after drying, placed on potato dextrose agar (PDA) medium, and incubated at 25±2 °C for one week. The fungal colonies of fluffy white growth with light orange pigment were isolated. For morphological characterization, a total of 4 pure cultures were isolated from leaves, collar region and root by single spore technique on carnation leaf agar (CLA) medium after 15 days of incubation at 25±2℃. Curved and thick-walled macroconidia with elongated or pointed apical characteristic foot-shaped basal cells were produced in sporodochia. Macroconidia with 5-7 septa measured 22.50-41.80 µm × 2.90-4.20 µm (n = 60). Thick, brown with roughened walls and subglobose ellipsoidal chlamydospores were observed in clumps or chains with the dimension of 5.8 to 10.8 µm (n = 20). On morphological characteristics, the fungus was identified as Fusarium equiseti (Corda) Sacc. according to Leslie and Summerell (2006). Two single spore isolates were used for molecular identification by amplifying ribosomal DNA of the internal transcribed spacer (ITS) region with ITS1/ITS4 primers (White et al. 1990) and for ß-tubulin gene region, primers T1/Bt-2b (O'Donnell and Cigelnik, 1997) were used. The obtained sequences were deposited in GenBank with accession numbers MW880179 and MW880198 from the ITS region and BLAST search in GenBank showed 100 and 98.11% alignment with previously published sequences of F. equiseti with accessions OM992323.1and MT558569.1 respectively. Accession number OM867571from the ß-tubulin region showed 100% sequence similarity with F. equiseti with accession MN653163.1. For pathogenicity, macroconidia from 2-week-old cultures on CLA medium were harvested to prepare spore suspension (1 × 106 conidia/ml). Koch's postulates were confirmed on nine bitter gourd plants (cv. Seminis-200) by applying spore suspension of fungal inoculum at 3-4 leaf stage separately on leaves by automizer, on collar region after making incision spore suspension was applied and in the root zone, 20ml spore suspension was added whereas distilled water was used as a control with three replications. Plants were kept under controlled conditions in the greenhouse with 65% to 75% humidity and the temperature was maintained at 32±2 °C for one week. After 7-8 days, inoculated plants began to exhibit symptoms of brown, necrotic leaf spots on the leaves of bitter gourd vines followed by yellowing of leaves that eventually turned brown. Roots showed slight discoloration and browning of vascular bundles and finally, the plants wilted after four weeks. while control plants remained symptomless. The symptoms resembled those noticed in the field. The fungus was re-isolated from leaves, collar region and roots, followed by morphological identification, and finally confirmed as F. equiseti. To the best of our knowledge, this is the first report of a leaf spot caused by F. equiseti in a bitter gourd from Pakistan. If the disease is not managed properly, it may cause a drastic effect on yield under favorable environmental conditions. The pathogen may also damage other cucurbitaceous crops cultivated in the area.

Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data.

Artificial intelligence is serving as an impetus in digital health, clinical support, and health informatics for an informed patient's outcome. Previous studies only consider classification accuracies of cardiotocographic (CTG) datasets and disregard computational time, which is a relevant parameter in a clinical environment. This paper proposes a modified deep neural algorithm to classify untapped pathological and suspicious CTG recordings with the desired time complexity. In our newly developed classification algorithm, AlexNet architecture is merged with support vector machines (SVMs) at the fully connected layers to reduce time complexity. We used an open-source UCI (Machine Learning Repository) dataset of cardiotocographic (CTG) recordings. We divided 2126 CTG recordings into 3 classes (Normal, Pathological, and Suspected), including 23 attributes that were dynamically programmed and fed to our algorithm. We employed a deep transfer learning (TL) mechanism to transfer prelearned features to our model. To reduce time complexity, we implemented a strategy wherein layers in the convolutional base were partially trained to leave others in the frozen states. We used an ADAM optimizer for the optimization of hyperparameters. The presented algorithm also outperforms the leading architectures (RCNNs, ResNet, DenseNet, and GoogleNet) with respect to real-time accuracies, sensitivities, and specificities of 99.72%, 96.67%, and 99.6%, respectively, making it a viable candidate for clinical settings after real-time validation.

Comprehensive Analysis of Network Slicing for the Developing Commercial Needs and Networking Challenges.

Network slicing (NS) is one of the most prominent next-generation wireless cellular technology use cases, promising to unlock the core benefits of 5G network architecture by allowing communication service providers (CSPs) and operators to construct scalable and customized logical networks. This, in turn, enables telcos to reach the full potential of their infrastructure by offering customers tailored networking solutions that meet their specific needs, which is critical in an era where no two businesses have the same requirements. This article presents a commercial overview of NS, as well as the need for a slicing automation and orchestration framework. Furthermore, it will address the current NS project objectives along with the complex functional execution of NS code flow. A summary of activities in important standards development groups and industrial forums relevant to artificial intelligence (AI) and machine learning (ML) is also provided. Finally, we identify various open research problems and potential answers to provide future guidance.

Match-Level Fusion of Finger-Knuckle Print and Iris for Human Identity Validation Using Neuro-Fuzzy Classifier.

Biometrics is the term for measuring human characteristics. If the term is divided into two parts, bio means life, and metric means measurement. The measurement of humans through different computational methods is performed to authorize a person. This measurement can be performed via a single biometric or by using a combination of different biometric traits. The combination of multiple biometrics is termed biometric fusion. It provides a reliable and secure authentication of a person at a higher accuracy. It has been introduced in the UIDIA framework in India (AADHAR: Association for Development and Health Action in Rural) and in different nations to figure out which biometric characteristics are suitable enough to authenticate the human identity. Fusion in biometric frameworks, especially FKP (finger-knuckle print) and iris, demonstrated to be a solid multimodal as a secure framework. The proposed approach demonstrates a proficient and strong multimodal biometric framework that utilizes FKP and iris as biometric modalities for authentication, utilizing scale-invariant feature transform (SIFT) and speeded up robust features (SURF). Log Gabor wavelet is utilized to extricate the iris feature set. From the extracted region, features are computed using principal component analysis (PCA). Both biometric modalities, FKP and iris, are combined at the match score level. The matching is performed using a neuro-fuzzy neural network classifier. The execution and accuracy of the proposed framework are tested on the open database Poly-U, CASIA, and an accuracy of 99.68% is achieved. The accuracy is higher compared to a single biometric. The neuro-fuzzy approach is also tested in comparison to other classifiers, and the accuracy is 98%. Therefore, the fusion mechanism implemented using a neuro-fuzzy classifier provides the best accuracy compared to other classifiers. The framework is implemented in MATLAB 7.10.

Implementation of an Intelligent Exam Supervision System Using Deep Learning Algorithms.

Examination cheating activities like whispering, head movements, hand movements, or hand contact are extensively involved, and the rectitude and worthiness of fair and unbiased examination are prohibited by such cheating activities. The aim of this research is to develop a model to supervise or control unethical activities in real-time examinations. Exam supervision is fallible due to limited human abilities and capacity to handle students in examination centers, and these errors can be reduced with the help of the Automatic Invigilation System. This work presents an automated system for exams invigilation using deep learning approaches i.e., Faster Regional Convolution Neural Network (RCNN). Faster RCNN is an object detection algorithm that is implemented to detect the suspicious activities of students during examinations based on their head movements, and for student identification, MTCNN (Multi-task Cascaded Convolutional Neural Networks) is used for face detection and recognition. The training accuracy of the proposed model is 99.5% and the testing accuracy is 98.5%. The model is fully efficient in detecting and monitoring more than 100 students in one frame during examinations. Different real-time scenarios are considered to evaluate the performance of the Automatic Invigilation System. The proposed invigilation model can be implemented in colleges, universities, and schools to detect and monitor student suspicious activities. Hopefully, through the implementation of the proposed invigilation system, we can prevent and solve the problem of cheating because it is unethical.

Early-Stage Alzheimer's Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains.

Alzheimer's Disease (AD) is a health apprehension of significant proportions that is negatively impacting the ageing population globally. It is characterized by neuronal loss and the formation of structures such as neurofibrillary tangles and amyloid plaques in the early as well as later stages of the disease. Neuroimaging modalities are routinely used in clinical practice to capture brain alterations associated with AD. On the other hand, deep learning methods are routinely used to recognize patterns in underlying data distributions effectively. This work uses Convolutional Neural Network (CNN) architectures in both 2D and 3D domains to classify the initial stages of AD into AD, Mild Cognitive Impairment (MCI) and Normal Control (NC) classes using the positron emission tomography neuroimaging modality deploying data augmentation in a random zoomed in/out scheme. We used novel concepts such as the blurring before subsampling principle and distant domain transfer learning to build 2D CNN architectures. We performed three binaries, that is, AD/NC, AD/MCI, MCI/NC and one multiclass classification task AD/NC/MCI. The statistical comparison revealed that 3D-CNN architecture performed the best achieving an accuracy of 89.21% on AD/NC, 71.70% on AD/MCI, 62.25% on NC/MCI and 59.73% on AD/NC/MCI classification tasks using a five-fold cross-validation hyperparameter selection approach. Data augmentation helps in achieving superior performance on the multiclass classification task. The obtained results support the application of deep learning models towards early recognition of AD.

A Novel Decentralized Blockchain Architecture for the Preservation of Privacy and Data Security against Cyberattacks in Healthcare.

Nowadays, in a world full of uncertainties and the threat of digital and cyber-attacks, blockchain technology is one of the major critical developments playing a vital role in the creative professional world. Along with energy, finance, governance, etc., the healthcare sector is one of the most prominent areas where blockchain technology is being used. We all are aware that data constitute our wealth and our currency; vulnerability and security become even more significant and a vital point of concern for healthcare. Recent cyberattacks have raised the questions of planning, requirement, and implementation to develop more cyber-secure models. This paper is based on a blockchain that classifies network participants into clusters and preserves a single copy of the blockchain for every cluster. The paper introduces a novel blockchain mechanism for secure healthcare sector data management, which reduces the communicational and computational overhead costs compared to the existing bitcoin network and the lightweight blockchain architecture. The paper also discusses how the proposed design can be utilized to address the recognized threats. The experimental results show that, as the number of nodes rises, the suggested architecture speeds up ledger updates by 63% and reduces network traffic by 10 times.

Singlet oxygen damages the function of Photosystem II in isolated thylakoids and in the green alga Chlorella sorokiniana.

Singlet oxygen (1O2) is an important damaging agent, which is produced during illumination by the interaction of the triplet excited state pigment molecules with molecular oxygen. In cells of photosynthetic organisms 1O2 is formed primarily in chlorophyll containing complexes, and damages pigments, lipids, proteins and other cellular constituents in their environment. A useful approach to study the physiological role of 1O2 is the utilization of external photosensitizers. In the present study, we employed a multiwell plate-based screening method in combination with chlorophyll fluorescence imaging to characterize the effect of externally produced 1O2 on the photosynthetic activity of isolated thylakoid membranes and intact Chlorella sorokiniana cells. The results show that the external 1O2 produced by the photosensitization reactions of Rose Bengal damages Photosystem II both in isolated thylakoid membranes and in intact cells in a concentration dependent manner indicating that 1O2 plays a significant role in photodamage of Photosystem II.

Proline is a quencher of singlet oxygen and superoxide both in in vitro systems and isolated thylakoids.

Proline is a versatile plant metabolite, which is produced in large amounts in plants exposed to osmotic and oxidative stress. Proline has been shown to provide protection against various reactive oxygen species (ROS), such as hydrogen peroxide and hydroxyl radicals. On the other hand, its protective effect against singlet oxygen has been debated, and it is considered ineffective against superoxide. Here we used various methods for the detection of singlet oxygen (electron paramagnetic resonance, EPR, spin trapping by 2,2,6,6-tetramethyl-4-piperidone, fluorescence probing by singlet oxygen sensor green, SOSG, and oxygen uptake due to chemical trapping) and superoxide (oxygen uptake due to oxygen reduction) in vitro and in isolated thylakoids. We demonstrated that proline does quench both singlet oxygen and superoxide in vitro. By comparing the effects of chemical scavengers and physical quenchers, we concluded that proline eliminates singlet oxygen via a physical mechanism, with a bimolecular quenching rate of ca. 1.5-4 106 M-1 s-1 . Our data also show that proline can eliminate superoxide in vitro in a process that is likely to proceed via an electron transfer reaction. We could also show that proline does quench both singlet oxygen and superoxide produced in isolated thylakoids. The scavenging efficiency of proline is relatively small on a molar basis, but considering its presence in high amounts in plant cells under stress conditions it may provide a physiologically relevant contribution to ROS scavenging, supplementing other nonenzymatic ROS scavengers of plant cells.

Energy Efficient UAV Flight Path Model for Cluster Head Selection in Next-Generation Wireless Sensor Networks.

Wireless sensor networks (WSNs) are one of the fundamental infrastructures for Internet of Things (IoTs) technology. Efficient energy consumption is one of the greatest challenges in WSNs because of its resource-constrained sensor nodes (SNs). Clustering techniques can significantly help resolve this issue and extend the network's lifespan. In clustering, WSN is divided into various clusters, and a cluster head (CH) is selected in each cluster. The selection of appropriate CHs highly influences the clustering technique, and poor cluster structures lead toward the early death of WSNs. In this paper, we propose an energy-efficient clustering and cluster head selection technique for next-generation wireless sensor networks (NG-WSNs). The proposed clustering approach is based on the midpoint technique, considering residual energy and distance among nodes. It distributes the sensors uniformly creating balanced clusters, and uses multihop communication for distant CHs to the base station (BS). We consider a four-layer hierarchical network composed of SNs, CHs, unmanned aerial vehicle (UAV), and BS. The UAV brings the advantage of flexibility and mobility; it shortens the communication range of sensors, which leads to an extended lifetime. Finally, a simulated annealing algorithm is applied for the optimal trajectory of the UAV according to the ground sensor network. The experimental results show that the proposed approach outperforms with respect to energy efficiency and network lifetime when compared with state-of-the-art techniques from recent literature.

An AI-enabled lightweight data fusion and load optimization approach for Internet of Things.

In the densely populated Internet of Things (IoT) applications, sensing range of the nodes might overlap frequently. In these applications, the nodes gather highly correlated and redundant data in their vicinity. Processing these data depletes the energy of nodes and their upstream transmission towards remote datacentres, in the fog infrastructure, may result in an unbalanced load at the network gateways and edge servers. Due to heterogeneity of edge servers, few of them might be overwhelmed while others may remain less-utilized. As a result, time-critical and delay-sensitive applications may experience excessive delays, packet loss, and degradation in their Quality of Service (QoS). To ensure QoS of IoT applications, in this paper, we eliminate correlation in the gathered data via a lightweight data fusion approach. The buffer of each node is partitioned into strata that broadcast only non-correlated data to edge servers via the network gateways. Furthermore, we propose a dynamic service migration technique to reconfigure the load across various edge servers. We assume this as an optimization problem and use two meta-heuristic algorithms, along with a migration approach, to maintain an optimal Gateway-Edge configuration in the network. These algorithms monitor the load at each server, and once it surpasses a threshold value (which is dynamically computed with a simple machine learning method), an exhaustive search is performed for an optimal and balanced periodic reconfiguration. The experimental results of our approach justify its efficiency for large-scale and densely populated IoT applications.

A Secured and Intelligent Communication Scheme for IIoT-enabled Pervasive Edge Computing.

Industrial Internet of Things (IIoT) ensures reliable and efficient data exchanges among the industrial processes using Artificial Intelligence (AI) within the cyber-physical systems. In the IIoT ecosystem, devices of industrial applications communicate with each other with little human intervention. They need to act intelligently to safeguard the data confidentiality and devices' authenticity. The ability to gather, process, and store real-time data depends on the quality of data, network connectivity, and processing capabilities of these devices. Pervasive Edge Computing (PEC) is gaining popularity nowadays due to the resource limitations imposed on the sensor-embedded IIoT devices. PEC processes the gathered data at the network edge to reduce the response time for these devices. However, PEC faces numerous research challenges in terms of secured communication, network connectivity, and resource utilization of the edge servers. To address these challenges, we propose a secured and intelligent communication scheme for PEC in an IIoT-enabled infrastructure. In the proposed scheme, forged identities of adversaries, i.e., Sybil devices, are detected by IIoT devices and shared with edge servers to prevent upstream transmission of their malicious data. Upon Sybil attack detection, each edge server executes a parallel Artificial Bee Colony (pABC) algorithm to perform optimal network configuration of IIoT devices. Each edge server performs the job migration to their neighboring servers for load balancing and better network performance, based on their processing and storage capabilities. The experimental results justify the efficiency of our proposed scheme in terms of Sybil attack detection, the convergence curves of our pABC algorithm, delay, throughput, and control overhead of data communication using PEC for IIoT.

Size-Based Sorting of Emulsion Droplets in Microfluidic Channels Patterned with Laser-Ablated Guiding Tracks.

We demonstrate an autonomous, high-throughput mechanism for sorting of emulsion droplets with different sizes concurrently flowing in a microfluidic Hele-Shaw channel. The aqueous droplets of varying radii suspended in olive oil are separated into different streamlines across the channel upon interaction with a shallow (depth ∼ 700 nm) inclined guiding track ablated into the polydimethylsiloxane-coated surface of the channel with focused femtosecond laser pulses. Specifically, the observed differences in the droplet trajectories along the guiding track arise due to the different scaling of the confinement force attracting the droplets into the track, fluid drag, and wall friction, with the droplet radius. In addition, the distance traveled by the droplets along the track also depends on the track width, with wider tracks providing more stable droplet guiding for any given droplet size. We systematically study the influence of the droplet size and velocity on the trajectory of the droplets in the channel and analyze the sensitivity of size-based droplet sorting for varying flow conditions. The droplet guiding and sorting experiments are complemented by modeling of the droplet motion in the channel flow using computational fluid dynamics simulations and a previously developed model of droplet guiding. Finally, we demonstrate a complete separation of droplets produced by fusion of two independent droplet streams at the inlet of the Hele-Shaw channel from unfused daughter droplets. The presented droplet sorting technique can find applications in the development of analytical and preparative microfluidic protocols.

Chloramphenicol enhances Photosystem II photodamage in intact cells of the cyanobacterium Synechocystis PCC 6803.

The effect of chloramphenicol, an often used protein synthesis inhibitor, in photosynthetic systems was studied on the rate of Photosystem II (PSII) photodamage in the cyanobacterium Synechocystis PCC 6803. Light-induced loss of PSII activity was compared in the presence of chloramphenicol and another protein synthesis inhibitor, lincomycin, by measuring the rate of oxygen evolution in Synechocystis 6803 cells. Our data show that the rate of PSII photodamage was significantly enhanced by chloramphenicol, at the usually applied 200 µg mL-1 concentration, relative to that obtained in the presence of lincomycin. Chloramphenicol-induced enhancement of photodamage has been observed earlier in isolated PSII membrane particles, and has been assigned to the damaging effect of chloramphenicol-mediated superoxide production (Rehman et al. 2016, Front Plant Sci 7:479). This effect points to the involvement of superoxide as damaging agent in the presence of chloramphenicol also in Synechocystis cells. The chloramphenicol-induced enhancement of photodamage was observed not only in wild-type Synechocystis 6803, which contains both Photosystem I (PSI) and PSII, but also in a PSI-less mutant which contains only PSII. Importantly, the rate of PSII photodamage was also enhanced by the absence of PSI when compared to that in the wild-type strain under all conditions studied here, i.e., without addition and in the presence of protein synthesis inhibitors. We conclude that chloramphenicol enhances photodamage mostly by its interaction with PSII, leading probably to superoxide production. The presence of PSI is also an important regulatory factor of PSII photodamage most likely via decreasing excitation pressure on PSII.

Spectral Efficiency Augmentation in Uplink Massive MIMO Systems by Increasing Transmit Power and Uniform Linear Array Gain.

Improved Spectral Efficiency (SE) is a prominent feature of Massive Multiple-Input and Multiple-Output systems. These systems are prepared with antenna clusters at receiver (Rx) and transmitter (Tx). In this paper, we examined a massive MIMO system to increase SE in each cell that ultimately improves the area throughput of the system. We are aiming to find appropriate values of average cell-density (D), available bandwidth (B), and SE to maximize area throughput because it is the function of these parameters. Likewise, a SE augmentation model was developed to attain an increased transmit power and antenna array gain. The proposed model also considers the inter-user interference from neighboring cells along with incident angles of desired and interfering users. Moreover, simulation results validate the proposed model that is implementable in real-time scenarios by realizing maximum SE of 12.79 bits/s/Hz in Line of Sight (LoS) and 12.69 bits/s/Hz in Non-Line of Sight (NLoS) scenarios, respectively. The proposed results also substantiate the SE augmentation because it is a linear function of transmit power and array gain while using the Uniform Linear Array (ULA) configuration. The findings of this work ensure the efficient transmission of information in future networks.

A Quality of Service-Aware Secured Communication Scheme for Internet of Things-Based Networks.

The Internet of Things (IoT) is an emerging technology that aims to enable the interconnection of a large number of smart devices and heterogeneous networks. Ad hoc networks play an important role in the designing of IoT-enabled platforms due to their efficient, flexible, low-cost and dynamic infrastructures. These networks utilize the available resources efficiently to maintain the Quality of Service (QoS) in a multi-hop communication. However, in a multi-hop communication, the relay nodes can be malicious, thus requiring a secured and reliable data transmission. In this paper, we propose a QoS-aware secured communication scheme for IoT-based networks (QoS-IoT). In QoS-IoT, a Sybil attack detection mechanism is used for the identification of Sybil nodes and their forged identities in multi-hop communication. After Sybil nodes detection, an optimal contention window (CW) is selected for QoS provisioning, that is, to achieve per-flow fairness and efficient utilization of the available bandwidth. In a multi-hop communication, the medium access control (MAC) layer protocols do not perform well in terms of fairness and throughput, especially when the nodes generate a large amount of data. It is because the MAC layer has no capability of providing QoS to prioritized or forwarding flows. We evaluate the performance of QoS-IoT in terms of Sybil attack detection, fairness, throughput and buffer utilization. The simulation results show that the proposed scheme outperforms the existing schemes and significantly enhances the performance of the network with a large volume of data. Moreover, the proposed scheme is resilient against Sybil attack.

The stress-induced SCP/HLIP family of small light-harvesting-like proteins (ScpABCDE) protects Photosystem II from photoinhibitory damages in the cyanobacterium Synechocystis sp. PCC 6803.

Small CAB-like proteins (SCPs) are single-helix light-harvesting-like proteins found in all organisms performing oxygenic photosynthesis. We investigated the effect of growth in moderate salt stress on these stress-induced proteins in the cyanobacterium Synechocystis sp. PCC 6803 depleted of Photosystem I (PSI), which expresses SCPs constitutively, and compared these cells with a PSI-less/ScpABCDE- mutant. SCPs, by stabilizing chlorophyll-binding proteins and Photosystem II (PSII) assembly, protect PSII from photoinhibitory damages, and in their absence electrons accumulate and will lead to ROS formation. The presence of 0.2 M NaCl in the growth medium increased the respiratory activity and other PSII electron sinks in the PSI-less/ScpABCDE- strain. We postulate that this salt-induced effect consumes the excess of PSII-generated electrons, reduces the pressure of the electron transport chain, and thereby prevents 1O2 production.