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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.
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Nanismo , Sequenciamento do Exoma , Mutação , Linhagem , Humanos , Feminino , Masculino , Nanismo/genética , Criança , Paquistão/epidemiologia , Predisposição Genética para Doença , Homozigoto , Fenótipo , Síndrome , Pré-Escolar , Adolescente , Estudos de Associação GenéticaRESUMO
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.
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Anaplasma marginale , Anaplasmose , Theileria , Carrapatos , Feminino , Bovinos , Animais , Ovinos , Anaplasma marginale/genética , Theileria/genética , Paquistão/epidemiologia , Anaplasma/genética , Prevalência , Carrapatos/microbiologia , Carrapatos/parasitologia , Filogenia , Anaplasmose/epidemiologia , Anaplasmose/microbiologiaRESUMO
This Special Issue is a collection of selected papers from the 10th and 11th International Conferences on Green and Human Information Technology (ICGHITs) [...].
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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.
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Acne Vulgar , Predisposição Genética para Doença , Adulto , Adolescente , Humanos , Incidência , Estudos de Casos e Controles , Estudos Transversais , Predisposição Genética para Doença/genética , Fatores de Risco , Glutationa Transferase/genética , Glutationa Transferase/metabolismo , Acne Vulgar/epidemiologia , Acne Vulgar/genética , Proteína Supressora de Tumor p53/genética , Glutationa S-Transferase pi/genéticaRESUMO
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.
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Transtorno do Espectro Autista , Internet das Coisas , Humanos , Criança , Transtorno do Espectro Autista/diagnóstico , Transtorno do Espectro Autista/psicologia , Gestos , Inteligência Artificial , FalaRESUMO
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.
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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.
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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.
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Transtorno do Espectro Autista , Dispositivos Eletrônicos Vestíveis , Algoritmos , Transtorno do Espectro Autista/diagnóstico , Criança , Gestos , Humanos , Aprendizado de MáquinaRESUMO
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.
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Dispositivos Eletrônicos Vestíveis , Algoritmos , Exercício Físico , Feminino , Humanos , Gravidez , Reconhecimento Psicológico , TempoRESUMO
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.
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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.
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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.
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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.
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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.
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Adenocarcinoma/cirurgia , Neoplasias do Colo/cirurgia , Colonoscopia/métodos , Excisão de Linfonodo/métodos , Linfonodos/patologia , Segunda Neoplasia Primária/cirurgia , Adenocarcinoma/patologia , Colectomia , Neoplasias do Colo/patologia , Humanos , Masculino , Pessoa de Meia-Idade , Segunda Neoplasia Primária/patologia , ProtectomiaRESUMO
The gelatinization and retrogradation characteristics of wheat starch affect the eating quality of Chinese-style food. Rapid Visco Analyzer (RVA) parameters have been widely used as important indicators to evaluate and improve the quality of wheat starch. However, the genetic basis of RVA parameters remains to be further explored. In the present study, a natural population was genotyped using 90K single nucleotide polymorphism (SNP) arrays, and the RVA parameters of this population grown in five environments were evaluated. The results showed that 22,068 high-quality SNP markers were identified and distributed unequally on the chromosomes. According to the genetic distance, 214 wheat materials were divided into four groups. Except for the pasting temperature (PTT), six parameters followed a normal distribution. Based on the general linear model, 969 significant association SNPs were detected by genome-wide association studies (GWAS), and chromosomes 7A and 2B had the most associated SNPs. Breakdown viscosity (BV) was associated with the most SNPs (n = 238), followed by PTT (n = 186), peak viscosity (PV; n = 156), trough viscosity (TV; n = 127), and final viscosity (FV; n = 126). According to the average linkage disequilibrium (LD), 33 stable quantitative trait loci (QTLs) were identified for single parameters in multiple environments, of which 12 were associated with BV, followed by peak time (PT; n = 8) and PTT (n = 7). On the other hand, 67 pleiotropic QTLs were identified for multiple parameters. Three candidate genes-TasbeIIa, TasbeI, and TassIIa-were screened for phenotyping analysis. The grain width and the weight of the TasbeIIa and TaSSIIa knockout (KO) lines were significantly lower than those of the TasbeI KO lines and the control (CK). The KO lines had smaller endosperm cells, smaller A-type starch granules, and higher amylose content. The TasbeI KO lines showed normal RVA curves, while the TasbeIIa KO lines showed flat curves. However, the TaSSIIa lines failed to paste under the RVA temperatures. Conclusively, the SNPs/QTLs significantly associated with the RVA parameters and genetic resources with novel haplotypes could be used to improve the quality of wheat starch.
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Introduction: Early studies exploring the physiological effects of space travel have indicated the body's capacity for reversible adaptation. However, the impact of long-duration spaceflight, exceeding 6 months, presents more intricate challenges. Effects on the Cardiovascular CV System: Extended exposure to microgravity and radiation profoundly affects the CV system. Notable phenomena include fluid shifts toward the head and modified arterial pressure. These changes disrupt blood pressure regulation and elevate cardiac output. Additionally, the loss of venous compression leads to a reduction in central venous pressure. Fluid and Plasma Volume Changes: The displacement of fluid from the vascular system to the interstitium, driven by baroreceptor stimulation, results in a 10%-15% decline in plasma volume. Cardiac Muscle and Hematocrit Variations: Intriguingly, despite potential increases in cardiac workload, cardiac muscle atrophy and perplexing variations in hematocrit levels have been observed. The mechanism underlying atrophy appears to involve a shift in protein synthesis from the endoplasmic reticulum to the mitochondria via mortalin-mediated mechanisms. Arrhythmias and QT Interval Prolongation: Instances of arrhythmias have been recurrently documented, although generally nonlethal, in both Russian and American space missions. Long-duration spaceflight has been associated with the prolongation of the QT interval, particularly in extended missions. Radiation Effects: Exposure of the heart to the proton and heavy ion radiation pervasive in deep space contributes to coronary artery degeneration, augmented aortic stiffness, and carotid intima thickening through collagen-mediated processes. Moreover, it accelerates the onset of atherosclerosis and triggers proinflammatory responses. Reentry and Postflight Challenges: Upon reentry, astronauts frequently experience orthostatic intolerance and altered sympathetic responses, which bear potential hazards in scenarios requiring rapid mobilization or evacuation. Conclusion: Consequently, careful monitoring of these cardiac risks is imperative for forthcoming missions. While early studies illuminate the adaptability of the body to space travel's challenges, the intricacies of long-duration missions and their effects on the CV system necessitate continued investigation and vigilance to ensure astronaut health and mission success.
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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.
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Pão , Triticum , Espécies Reativas de Oxigênio/metabolismo , Melhoramento Vegetal , Grão Comestível/metabolismoRESUMO
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.
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Germinação/efeitos dos fármacos , Polietilenoglicóis/farmacologia , Plântula/efeitos dos fármacos , Triticum/genética , Secas , Genótipo , Pressão Osmótica , Análise de Componente Principal , Plântula/genética , Plântula/crescimento & desenvolvimento , Sementes/genética , Sementes/crescimento & desenvolvimento , Triticum/crescimento & desenvolvimentoRESUMO
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.