Germination responses of Lens Culiunaris L. seeds to osmotic potentials at cardinal temperatures using hydrothermal time model.

BACKGROUND: Lentil is a significant legume that are consumed as a staple food and have a significant economic impact around the world. The purpose of the present research on lentil was to assess the hydrothermal time model's capacity to explain the dynamics of Lens culinaris L. var. Markaz-09 seed germination, as well as to ascertain the germination responses at various sub-optimal temperatures (T) and water potentials (Ψ). In order to study lentil seed germination (SG) behavior at variable water potentials (Ψs) and temperatures (Ts). A lab experiment employing the hydrothermal time model was created. Seeds were germinated at six distinct temperatures: 15 0С, 20 0С, 25 0С, 30 0С, 35 0С, and 40 0С, with five Ψs of 0, -0.3, -0.6, -0.9, and - 1.2 MPa in a PEG-6000 (Polyethylene glycol 6000) solution. RESULTS: The results indicated that the agronomic parameters like Germination index (GI), Germination energy (GE), Timson germination index (TGI), were maximum in 25 0C at (-0.9 MPa) and lowest at 40 0C in 0 MPa. On other hand, mean germination time (MGT) value was highest at 15 0C in -1.2 MPa and minimum at 40 0C in (-0.6 MPa) while Mean germination rate (MGR) was maximum at 40 0C in (0 MPa) and minimum at 15 0C in (-0.6 MPa). CONCLUSIONS: The HTT model eventually defined the germination response of Lens culinaris L. var. Markaz-09 (Lentil) for all Ts and Ψs, allowing it to be employed as a predictive tool in Lens culinaris L. var. Markaz-09 (Lentil) seed germination simulation models.

Speech Emotion Recognition Using Convolution Neural Networks and Multi-Head Convolutional Transformer.

Speech emotion recognition (SER) is a challenging task in human-computer interaction (HCI) systems. One of the key challenges in speech emotion recognition is to extract the emotional features effectively from a speech utterance. Despite the promising results of recent studies, they generally do not leverage advanced fusion algorithms for the generation of effective representations of emotional features in speech utterances. To address this problem, we describe the fusion of spatial and temporal feature representations of speech emotion by parallelizing convolutional neural networks (CNNs) and a Transformer encoder for SER. We stack two parallel CNNs for spatial feature representation in parallel to a Transformer encoder for temporal feature representation, thereby simultaneously expanding the filter depth and reducing the feature map with an expressive hierarchical feature representation at a lower computational cost. We use the RAVDESS dataset to recognize eight different speech emotions. We augment and intensify the variations in the dataset to minimize model overfitting. Additive White Gaussian Noise (AWGN) is used to augment the RAVDESS dataset. With the spatial and sequential feature representations of CNNs and the Transformer, the SER model achieves 82.31% accuracy for eight emotions on a hold-out dataset. In addition, the SER system is evaluated with the IEMOCAP dataset and achieves 79.42% recognition accuracy for five emotions. Experimental results on the RAVDESS and IEMOCAP datasets show the success of the presented SER system and demonstrate an absolute performance improvement over the state-of-the-art (SOTA) models.

Population genetic analyses inferred a limited genetic diversity across the pvama-1 DI domain among Plasmodium vivax isolates from Khyber Pakhtunkhwa regions of Pakistan.

BACKGROUND: Plasmodium vivax apical membrane antigen-1 (pvama-1) is an important vaccine candidate against Malaria. The genetic composition assessment of pvama-1 from wide-range geography is vital to plan the antigen based vaccine designing against Malaria. METHODS: The blood samples were collected from 84 P. vivax positive malaria patients from different districts of Khyber Pakhtunkhwa (KP) province of Pakistan. The highly polymorphic and immunogenic domain-I (DI) region of pvama-1 was PCR amplified and DNA sequenced. The QC based sequences raw data filtration was done using DNASTAR package. The downstream population genetic analyses were performed using MEGA4, DnaSP, Arlequin v3.5 and Network.5 resources. RESULTS: The analyses unveiled total 57 haplotypes of pvama-1 (DI) in KP samples with majorly prevalent H-14 and H-5 haplotypes. Pairwise comparative population genetics analyses identified limited to moderate genetic distinctions among the samples collected from different districts of KP, Pakistan. In context of worldwide available data, the KP samples depicted major genetic differentiation against the Korean samples with Fst = 0.40915 (P-value = 0.0001), while least distinction was observed against Indian and Iranian samples. The statistically significant negative values of Fu and Li's D* and F* tests indicate the evidence of population expansion and directional positive selection signature. The slow LD decay across the nucleotide distance in KP isolates indicates low nucleotide diversity. In context of reference pvama-1 sequence, the KP samples were identified to have 09 novel non-synonymous single nucleotide polymorphisms (nsSNPs), including several trimorphic and tetramorphic substitutions. Few of these nsSNPs are mapped within the B-cell predicted epitopic motifs of the pvama-1, and possibly modulate the immune response mechanism. CONCLUSION: Low genetic differentiation was observed across the pvama-1 DI among the P. vivax isolates acquired from widespread regions of KP province of Pakistan. The information may implicate in future vaccine designing strategies based on antigenic features of pvama-1.

Genomic miscellany and allelic frequencies of Plasmodium falciparum msp-1, msp-2 and glurp in parasite isolates.

INTRODUCTION: The genomic miscellany of malaria parasites can help inform the intensity of malaria transmission and identify potential deficiencies in malaria control programs. This study was aimed at investigating the genomic miscellany, allele frequencies, and MOI of P. falciparum infection. METHODS: A total of 85 P. falciparum confirmed isolates out of 100 were included in this study that were collected from P. falciparum patients aged 4 months to 60 years in nine districts of Khyber Pakhtunkhwa Province. Parasite DNA was extracted from 200µL whole blood samples using the Qiagen DNA extraction kit following the manufacturer's instructions. The polymorphic regions of msp-1, msp-2 and glurp loci were genotyped using nested PCR followed by gel electrophoresis for amplified fragments identification and subsequent data analysis. RESULTS: Out of 85 P. falciparum infections detected, 30 were msp-1 and 32 were msp-2 alleles specific. Successful amplification occurred in 88.23% (75/85) isolates for msp-1, 78.9% (67/85) for msp-2 and 70% (60/85) for glurp gene. In msp-1, the K1 allelic family was predominantly prevalent as 66.66% (50/75), followed by RO33 and MAD20. The frequency of samples with single infection having only K1, MAD20 and RO33 were 21.34% (16/75), 8% (6/75), and 10.67% (8/75), respectively. In msp-2, both the FC27 and 3D7 allelic families revealed almost the same frequencies as 70.14% (47/67) and 67.16% (45/67), respectively. Nine glurp RII region alleles were identified in 60 isolates. The overall mean multiplicity of infection for msp genes was 1.6 with 1.8 for msp-1 and 1.4 for msp-2, while for glurp the MOI was 1.03. There was no significant association between multiplicity of infection and age groups (Spearman's rank coefficient = 0.050; P = 0.6) while MOI and parasite density correlated for only msp-2 allelic marker. CONCLUSIONS: The study showed high genetic diversity and allelic frequency with multiple clones of msp-1, msp-2 and glurp in P. falciparum isolates in Khyber Pakhtunkhwa, Pakistan. In the present study the genotype data may provide valuable information essential for monitoring the impact of malaria eradication efforts in this region.

Leveraging Blockchain Technology for Secure Energy Trading and Least-Cost Evaluation of Decentralized Contributions to Electrification in Sub-Saharan Africa.

The International Energy Agency has projected that the total energy demand for electricity in sub-Saharan Africa (SSA) is expected to rise by an average of 4% per year up to 2040. It implies that ~620 million people are living without electricity in SSA. Going with the 2030 vision of the United Nations that electricity should be accessible to all, it is important that new technology and methods are provided. In comparison to other nations worldwide, smart grid (SG) is an emerging technology in SSA. SG is an information technology-enhanced power grid, which provides a two-way communication network between energy producers and customers. Also, it includes renewable energy, smart meters, and smart devices that help to manage energy demands and reduce energy generation costs. However, SG is facing inherent difficulties, such as energy theft, lack of trust, security, and privacy issues. Therefore, this paper proposes a blockchain-based decentralized energy system (BDES) to accelerate rural and urban electrification by improving service delivery while minimizing the cost of generation and addressing historical antipathy and cybersecurity risk within SSA. Additionally, energy insufficiency and fixed pricing schemes may raise concerns in SG, such as the imbalance of order. The paper also introduces a blockchain-based energy trading system, which includes price negotiation and incentive mechanisms to address the imbalance of order. Moreover, existing models for energy planning do not consider the effect of fill rate (FR) and service level (SL). A blockchain levelized cost of energy (BLCOE) is proposed as the least-cost solution that measures the impact of energy reliability on generation cost using FR and SL. Simulation results are presented to show the performance of the proposed model and the least-cost option varies with relative energy generation cost of centralized, decentralized and BDES infrastructure. Case studies of Burkina Faso, Cote d'Ivoire, Gambia, Liberia, Mali, and Senegal illustrate situations that are more suitable for BDES. For other SSA countries, BDES can cost-effectively service a large population and regions. Additionally, BLCOE reduces energy costs by approximately 95% for battery and 75% for the solar modules. The future BLCOE varies across SSA on an average of about 0.049 $/kWh as compared to 0.15 $/kWh of an existing system in the literature.