Clinical and genetic investigation of 14 families with various forms of short stature syndromes.

Skeletal dysplasias are a heterogeneous group of disorders presenting mild to lethal defects. Several factors, such as genetic, prenatal, and postnatal environmental may contribute to reduced growth. Fourteen families of Pakistani origin, presenting the syndromic form of short stature either in the autosomal recessive or autosomal dominant manner were clinically and genetically investigated to uncover the underlying genetic etiology. Homozygosity mapping, whole exome sequencing, and Sanger sequencing were used to search for the disease-causing gene variants. In total, we have identified 13 sequence variants in 10 different genes. The variants in the HSPG2 and XRCC4 genes were not reported previously in the Pakistani population. This study will expand the mutation spectrum of the identified genes and will help in improved diagnosis of the syndromic form of short stature in the local population.

Leading report of molecular prevalence of tick borne Anaplasma marginale and Theileria ovis in yaks (Bos grunniens) from Pakistan.

Domestic yak (Bos grunniens) is an economically important feature of the mountainous region of Gilgit-Baltistan in Pakistan where agriculture is restricted and yaks play multiple roles which includes being a source of milk, meat, hides, fuel and power. However little is known about the parasitic infections in Pakistani yaks. Aim of this research was to report the prevalence and genetic diversity of protozoa parasite (Theileria ovis, 18 S rDNA gene was targeted) and an obligate bacterium (Anaplasma marginale, msp-1 gene was amplified) in the blood that was sampled from 202 yaks collected from four districts in Gilgit-Baltistan during January 2023 till January 2024. Results revealed that 6/202 (3%) yaks were of Theileria ovis while 8/202 (4%) were Anaplasma marginale infected. Positive PCR products of both parasites were confirmed by DNA sequencing and their similarity with previously available pathogen sequences was determined by BLAST analysis. Phylogenetic tree indicated that isolates of both parasites displayed genetic. Anaplasma marginale infection varied with the sampling districts and Shigar district had the highest rate of bacterial infection. Cows were significantly more prone to Theileria ovis infection than bulls. Calf and hybrid yaks were more prone to Anaplasma marginale infection. In conclusion, this is the first report that yaks residing the Gilgit-Baltistan region in Pakistan are infected with Theileria ovis and Anaplasma marginale. Similar larger scales studies are recommended in various regions of Gilgit-Baltistan to document the infection rates of these parasites to formulate strategies that will lead to the effective control of these pathogens.

Special Issue "Emerging AI+X-Based Sensor and Networking Technologies including Selected Papers from ICGHIT 2022-2023".

This Special Issue is a collection of selected papers from the 10th and 11th International Conferences on Green and Human Information Technology (ICGHITs) [...].

Absence of GSTT1 and polymorphisms in GSTP1 and TP53 are associated with the incidence of acne vulgaris.

BACKGROUNDS: Acne vulgaris is a chronic inflammatory skin disease of the pilosebaceous unit affecting most teenagers and numerous adults throughout the world. The present study was designed to assess the association of the presence or absence of GSTM1, GSTT1, and single nucleotide polymorphisms rs1695 in GSTP1 and rs1042522 in TP53 gene with acne vulgaris. METHODS: The cross-sectional case-control study was conducted at the Institute of Zoology from May 2020 to March 2021 and included acne vulgaris patients (N = 100) and controls (N = 100) enrolled in Dera Ghazi Khan district, Pakistan. Multiplex and tetra-primer amplification refractory mutation system-polymerase chain reactions were applied to investigate the genotype in analyzed genes. The association of rs1695 and rs1042522 with acne vulgaris was studied either individually or in various combinations with GATM1 and T1. RESULTS: A significant association of absence of GSTT1 and mutant genotype at rs1695 (GG) and at rs1042522 (CC) in GSTP1 and TP53, respectively, was found to be associated with acne vulgaris in enrolled subjects. Subjects aged 10-25 years and smokers were more susceptible to acne vulgaris. CONCLUSION: Our results suggest that genotypes of glutathione S-transferases (GSTs) and TP53 are involved in protection against oxidative stress and may influence disease progression in acne vulgaris.

Fusion-Based Body-Worn IoT Sensor Platform for Gesture Recognition of Autism Spectrum Disorder Children.

The last decade's developments in sensor technologies and artificial intelligence applications have received extensive attention for daily life activity recognition. Autism spectrum disorder (ASD) in children is a neurological development disorder that causes significant impairments in social interaction, communication, and sensory action deficiency. Children with ASD have deficits in memory, emotion, cognition, and social skills. ASD affects children's communication skills and speaking abilities. ASD children have restricted interests and repetitive behavior. They can communicate in sign language but have difficulties communicating with others as not everyone knows sign language. This paper proposes a body-worn multi-sensor-based Internet of Things (IoT) platform using machine learning to recognize the complex sign language of speech-impaired children. Optimal sensor location is essential in extracting the features, as variations in placement result in an interpretation of recognition accuracy. We acquire the time-series data of sensors, extract various time-domain and frequency-domain features, and evaluate different classifiers for recognizing ASD children's gestures. We compare in terms of accuracy the decision tree (DT), random forest, artificial neural network (ANN), and k-nearest neighbour (KNN) classifiers to recognize ASD children's gestures, and the results showed more than 96% recognition accuracy.

Information-Centric Networking in Wireless Environments: Security Risks and Challenges.

Information-Centric Networking (ICN) has emerged as a paradigm to cope with the lack of built-in security primitives and efficient mechanisms for content distribution of today's Internet. However, deploying ICN in a wireless environment poses a different set of challenges compared to a wired environment, especially when it comes to security. In this paper, we present the security issues that may arise and the attacks that may occur from different points of view when ICN is deployed in wireless environments. The discussed attacks may target both applications and the ICN network itself by exploiting elements of the ICN architecture, such as content names and in-network content caches. Furthermore, we discuss potential solutions to the presented issues and countermeasures to the presented attacks. Finally, we identify future research opportunities and directions.

Advanced Physical-Layer Technologies for Beyond 5G Wireless Communication Networks.

Fifth-generation (5G) networks will not satisfy the requirements of the latency, bandwidth, and traffic density in 2030 and beyond, and next-generation wireless communication networks with revolutionary enabling technologies will be required. Beyond 5G (B5G)/sixth-generation (6G) networks will achieve superior performance by providing advanced functions such as ultralow latency, ultrahigh reliability, global coverage, massive connectivity, and better intelligence and security levels. Important aspects of B5G/6G networks require the modification and exploitation of promising physical-layer technologies. This Special Issue (SI) presents research efforts to identify and discuss the novel techniques, technical challenges, and promising solution methods of physical-layer technologies with a vision of potential involvement in the B5G/6G era. In particular, this SI presents innovations and concepts, including nonorthogonal multiple access, massive multiple-input multiple-output (MIMO), energy harvesting, hybrid satellite terrestrial relays, Internet of Things-based home automation, millimeter-wave bands, device-to-device communication, and artificial-intelligence or machine-learning techniques. Further, this SI covers the proposed solutions, including MIMO antenna design, modulation detection, interference management, hybrid precoding, and statistical beamforming along with their performance improvements in terms of performance metrics, including bit error rate, outage probability, ergodic sum rate, spectrum efficiency, and energy efficiency.

An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks.

In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.

Wearable-Sensors-Based Platform for Gesture Recognition of Autism Spectrum Disorder Children Using Machine Learning Algorithms.

Autistic people face many challenges in various aspects of daily life such as social skills, repetitive behaviors, speech, and verbal communication. They feel hesitant to talk with others. The signs of autism vary from one individual to another, with a range from mild to severe. Autistic children use fewer communicative gestures compared with typically developing children (TD). With time, the parents may learn their gestures and understand what is occurring in their child's mind. However, it is difficult for other people to understand their gestures. In this paper, we propose a wearable-sensors-based platform to recognize autistic gestures using various classification techniques. The proposed system defines, monitors, and classifies the gestures of the individuals. We propose using wearable sensors that transmit their data using a Bluetooth interface to a data acquisition and classification server. A dataset of 24 gestures is created by 10 autistic children performing each gesture about 10 times. Time- and frequency-domain features are extracted from the sensors' data, which are classified using k-nearest neighbor (KNN), decision tree, neural network, and random forest models. The main objective of this work is to develop a wearable-sensor-based IoT platform for gesture recognition in children with autism spectrum disorder (ASD). We achieve an accuracy of about 91% with most of the classifiers using dataset cross-validation and leave-one-person-out cross-validation.

A Framework for Maternal Physical Activities and Health Monitoring Using Wearable Sensors.

We propose a physical activity recognition and monitoring framework based on wearable sensors during maternity. A physical activity can either create or prevent health issues during a given stage of pregnancy depending on its intensity. Thus, it becomes very important to provide continuous feedback by recognizing a physical activity and its intensity. However, such continuous monitoring is very challenging during the whole period of maternity. In addition, maintaining a record of each physical activity, and the time for which it was performed, is also a non-trivial task. We aim at such problems by first recognizing a physical activity via the data of wearable sensors that are put on various parts of body. We avoid the use of smartphones for such task due to the inconvenience caused by wearing it for activities such as "eating". In our proposed framework, a module worn on body consists of three sensors: a 3-axis accelerometer, 3-axis gyroscope, and temperature sensor. The time-series data from these sensors are sent to a Raspberry-PI via Bluetooth Low Energy (BLE). Various statistical measures (features) of this data are then calculated and represented in features vectors. These feature vectors are then used to train a supervised machine learning algorithm called classifier for the recognition of physical activity from the sensors data. Based on such recognition, the proposed framework sends a message to the care-taker in case of unfavorable situation. We evaluated a number of well-known classifiers on various features developed from overlapped and non-overlapped window size of time-series data. Our novel dataset consists of 10 physical activities performed by 61 subjects at various stages of maternity. On the current dataset, we achieve the highest recognition rate of 89% which is encouraging for a monitoring and feedback system.

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.

Towards Network Lifetime Enhancement of Resource Constrained IoT Devices in Heterogeneous Wireless Sensor Networks.

The participating nodes in Wireless Sensor Networks (WSNs) are usually resource-constrained in terms of energy consumption, storage capacity, computational capability, and communication range. Energy is one of the major constraints which requires an efficient mechanism that takes into account the energy consumption of nodes to prolong the network lifetime. Particularly in the large scale heterogeneous WSNs, this challenge becomes more critical due to high data collection rate and increased number of transmissions. To this end, clustering is one of the most popular mechanisms which is being used to minimize the energy consumption of nodes and prolong the lifetime of the network. In this paper, therefore, we propose a robust clustering mechanism for energy optimization in heterogeneous WSNs. In the proposed scheme, nodes declare themselves as cluster head (CH) based on available resources such as residual energy, available storage and computational capability. The proposed scheme employs the multi criteria decision making technique named as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) which allows the child nodes to select the optimal CH among several potential CH candidates. Moreover, we also propose mechanisms such as CH-acquaintanceship and CH-friendship in order to prolong the network lifetime. Simulation results show that our proposed scheme minimizes the control overhead, reduces the power consumption and enhances overall lifetime of the network by comparing with the most recent and relevant proposed protocol for WSNs.

Hierarchical Name-Based Mechanism for Push-Data Broadcast Control in Information-Centric Multihop Wireless Networks.

By design, Named Data Networking (NDN) supports pull-based traffic, where content is retrieved only upon consumer request. However, some of the use cases (i.e., emergency situations) in the Internet of Things (IoT) requires push-based traffic, where a producer broadcasts the data based on the emergency situation without any consumer request. Therefore, it is necessary to modify the existing NDN forwarding engine when designing for an IoT scenario. Although solutions are provided to enable push-based traffic in IoT, the main solutions in the current literature lack data broadcast control design. Moreover, the existing solutions use an additional interest messages exchange, which creates extra overheads in the network, thereby resulting in higher delay and lower throughput. In this paper, therefore, we propose a name-based push-data broadcast control scheme for IoT systems, and consider two scenarios, i.e., smart buildings and vehicular networks. The proposed scheme consists of a robust content namespace design, device namespace design, and minor amendments to the data packet format and unsolicited data policy of the forwarding engine as well. The evaluation is carried out for both scenarios. Simulation experiments show that the proposed scheme outperforms the recent proposed schemes in terms of total number of data packets processed in the network, total energy consumption, and average delay in the network by varying the number of data packets per 2 s and varying vehicle speed.

NINQ: Name-Integrated Query Framework for Named-Data Networking of Things.

Information-Centric Networking (ICN) is a paradigm shift from host-to-host Internet Protocol (IP)-based communication to content-based communication. In ICN, the content-retrieval process employs names that are given through different naming schemes such as hierarchical, flat, attribute, and hybrid. Among different ICN architectures, Named-Data Networking (NDN) has gained much interest in the research community and is actively being explored for the Internet of Things (IoT) and sensor networks, and follows a hierarchical naming format. NDN protocol follows a pull-based communication model where the content consumer gets content irrespective of the location of the content provider. The content provider in NDN and sensor networks can be considered to be a distributed database that monitors or controls the environment and caches the sensed data or controls information into their memory. The proposed Name-INtegrated Query (NINQ) framework for NDN-based IoT provides a flexible, expressive, and secure query mechanism that supports content retrieval as well as control and configuration command exchange among various nodes in a smart building. Different use cases are presented in this paper that expand on the behavior of proposed query framework in different scenarios. Simulation results of data collection and exchange of control commands show that proposed query framework significantly improves Interest Satisfaction Rate (ISR), Command Satisfaction Rate (CSR), energy efficiency, and average delay. Moreover, it is evident from the simulation results that proposed query framework significantly reduces the number of transmissions in the network in both data collection and exchange of control command scenarios, which improves the network performance.

TaMADS29 interacts with TaNF-YB1 to synergistically regulate early grain development in bread wheat.

Grain development is a crucial determinant of yield and quality in bread wheat (Triticum aestivum L.). However, the regulatory mechanisms underlying wheat grain development remain elusive. Here we report how TaMADS29 interacts with TaNF-YB1 to synergistically regulate early grain development in bread wheat. The tamads29 mutants generated by CRISPR/Cas9 exhibited severe grain filling deficiency, coupled with excessive accumulation of reactive oxygen species (ROS) and abnormal programmed cell death that occurred in early developing grains, while overexpression of TaMADS29 increased grain width and 1,000-kernel weight. Further analysis revealed that TaMADS29 interacted directly with TaNF-YB1; null mutation in TaNF-YB1 caused grain developmental deficiency similar to tamads29 mutants. The regulatory complex composed of TaMADS29 and TaNF-YB1 exercises its possible function that inhibits the excessive accumulation of ROS by regulating the genes involved in chloroplast development and photosynthesis in early developing wheat grains and prevents nucellar projection degradation and endosperm cell death, facilitating transportation of nutrients into the endosperm and wholly filling of developing grains. Collectively, our work not only discloses the molecular mechanism of MADS-box and NF-Y TFs in facilitating bread wheat grain development, but also indicates that caryopsis chloroplast might be a central regulator of grain development rather than merely a photosynthesis organelle. More importantly, our work offers an innovative way to breed high-yield wheat cultivars by controlling the ROS level in developing grains.

The impact of PEG-induced drought stress on seed germination and seedling growth of different bread wheat (Triticum aestivum L.) genotypes.

Wheat is an important crop, used as staple food in numerous countries around the world. However, wheat productivity is low in the developing world due to several biotic and abiotic stresses, particularly drought stress. Non-availability of drought-tolerant wheat genotypes at different growth stages is the major constraint in improving wheat productivity in the developing world. Therefore, screening/developing drought-tolerant genotypes at different growth stages could improve the productivity of wheat. This study assessed seed germination and seedling growth of eight wheat genotypes under polyethylene glycol (PEG)-induced stress. Two PEG-induced osmotic potentials (i.e., -0.6 and -1.2 MPa) were included in the study along with control (0 MPa). Wheat genotypes included in the study were 'KLR-16', 'B6', 'J10', '716', 'A12', 'Seher', 'KTDH-16', and 'J4'. Data relating to seed germination percentage, root and shoot length, fresh and dry weight of roots and shoot, root/shoot length ratio and chlorophyll content were recorded. The studied parameters were significantly altered by individual and interactive effects of genotypes and PEG-induced osmotic potentials. Seed germination and growth parameters were reduced by osmotic potentials; however, huge differences were noted among genotypes. A reduction of 32.83 to 53.50% was recorded in seed germination, 24.611 to 47.75% in root length, 37.83 to 53.72% in shoot length, and 53.35 to 65.16% in root fresh weight. The genotypes, 'J4', 'KLR-16' and 'KTDH-16', particularly 'J4' better tolerated increasing osmotic potentials compared to the rest of the genotypes included in the study. Principal component analysis segregated these genotypes from the rest of the genotypes included in the study indicated that these can be used in the future studies to improve the drought tolerance of wheat crop. The genotype 'J4' can be used as a breeding material to develop drought resistant wheat genotypes.

Impact of varying levels of soil salinity on emergence, growth and biochemical attributes of four Moringa oleifera landraces.

Salinity in soil and water is one of the environmental factors that severely hinder the crop growth and production particularly in arid and semi-arid regions. A pot experiment was conducted to investigate the impact of salinity levels (1.5 dS m-1, 3.5 dS m-1, 7.5 dS m-1 and 11.5 dS m-1) on emergence, growth and biochemical traits of moringa landraces under completely randomized design having three replications. Four landraces of Moringa oleifera (Faisalabad black seeded moringa [MFB], Patoki black seeded moringa [MPB], Faisalabad white seeded moringa [MFW] and Rahim Yar Khan black seeded moringa [MRB]) were selected for experimentation. All the salinity levels significantly affected the emergence parameters (time to emergence start, time to 50% emergence, mean emergence time, emergence index and final emergence percentage) of moringa landraces. However, 1.5 dS m-1 and 3.5 dS m-1 were found more favorable. Higher salinity levels (7.5 dS m-1 and 11.5 dS m-1) significantly minimized the root surface area, root projected area, root volume and root density as compared to 1.5 dS m-1, 3.5 dS m-1. Number of branches, leaves, leaflets and leaf length were also adversely affected by 7.5 dS m-1 and 11.5 dS m-1. Maximum seedling fresh and dry weights, and seedling length were recorded at 1.5 dS m-1 followed by 3.5 dS m-1. Chlorophyll a and b contents, carotenoids and membrane stability index were also observed highest at salinity level of 1.5 dS m-1. In case of moringa landraces, MRB performed better regarding emergence attributes, growth parameters, and biochemical analysis followed by MFW as compared to MFB and MPB. Moringa landraces i.e. MRB and MFW were found more tolerant to salinity stress as compared to MFB and MPB.

Field screening of diverse wheat germplasm for determining their adaptability to semi-arid climatic conditions.

Wheat (Triticum aestivum L.) is an important staple food crop for one third of global population and important crop for securing future food security. Rapid changes in the climate on global scale could be a threat for future food security. This situation urges plant breeders to explore the genetic potential of existing wheat germplasm. This study screened forty diverse wheat genotypes for their yield under two different agroclimatic conditions, i.e., Layyah and Dera Ghazi Khan, Pakistan. Data relating to plant height, peduncle length, flag leaf area, spike length, number of spikelets, number of grains per spike, thousand grain weight, chlorophyll content and grain yield were recorded. The tested wheat genotypes significantly differed for grain yield and related traits. Grain yield was positively correlated with plant height, spike length, spike number, flag leaf length, number of grains per spike, and 1000-grain weight. Biplot obtained from the cluster analysis by Euclidean method grouped the studied genotypes in 3 different groups. The genotypes exhibited 10.77% variability within quadrants, whereas 72.36% variability was recorded between quadrants according to clustering. Dendrogram grouped the tested genotypes into two main clusters. The main cluster "I" comprised of 2 genotypes, i.e., 'Seher-2006' and 'AS-2002'. The cluster "II" contained 38 genotypes based on Euclidian values. Genotypes within same cluster had smaller D2 values compared to those belonging to other clusters. The genetic relationships of genotypes provide useful information for breeding programs. Overall, the results revealed that genotypes 'Line 9733', 'Bhakar-2002', 'Line A9' and 'SYN-46' had better yield and yield stability under climatic conditions of southern Punjab. Therefore, these genotypes could be recommended for general cultivation in the study region.

Seed germination ecology of Conyza sumatrensis populations stemming from different habitats and implications for management.

Conyza sumatrensis (Retz.) E. H. Walker is an obnoxious weed, emerging as an invasive species globally. Seed germination biology of four populations of the species stemming from arid, semi-arid, temperate, and humid regions was determined in this study. Seed germination was recorded under six different environmental cues (i.e., light/dark periods, constant and alternating day and night temperatures, pH, salinity, and osmotic potential levels) in separate experiment for each cue. Populations were main factor, whereas levels of each environmental cue were considered as sub-factor. The impact of seed burial depths on seedling emergence was inferred in a greenhouse pot experiment. Seed germination was recorded daily and four germination indices, i.e., seed germination percentage, mean germination time, time to reach 50% germination, and mean daily germination were computed. Tested populations and levels of different environmental cues had significant impact on various seed germination indices. Overall, seeds stemming from arid and semi-arid regions had higher seed germination potential under stressful and benign environmental conditions compared to temperate and humid populations. Seed of all populations required a definite light period for germination and 12 hours alternating light and dark period resulted in the highest seed germination. Seed germination of all populations occurred under 5-30°C constant and all tested alternate day and night temperatures. However, the highest seed germination was recorded under 20°C. Seeds of arid and semi-arid populations exhibited higher germination under increased temperature, salinity and osmotic potential levels indicating that maternal environment strongly affected germination traits of the tested populations. The highest seed germination of the tested populations was noted under neutral pH, while higher and lower pH than neutral had negative impact on seed germination. Arid and semi-arid populations exhibited higher seed germination under increased pH compared to temperate and humid populations. Seed burial depth had a significant effect on the seedling emergence of all tested populations. An initial increase was noted in seedling emergence percentage with increasing soil depth. However, a steep decline was recorded after 2 cm seed burial depth. These results indicate that maternal environment strongly mediates germination traits of different populations. Lower emergence from >4 cm seed burial depth warrants that deep burial of seeds and subsequent zero or minimum soil disturbance could aid the management of the species in agricultural habitats. However, management strategies should be developed for other habitats to halt the spread of the species.