Immediate and Early Postnatal Care for Mothers and Newborns in Rural Bangladesh

The study evaluated the impact of essential newborn-care interventions at the household level in the Saving Newborn Lives project areas. Two household surveys were conducted following the 30-cluster sampling method using a structured questionnaire in 2002 (baseline) and 2004 (endline) respectively. In total, 3,325 mothers with children aged less than one year in baseline and 3,110 mothers in endline from 10 sub-districts were interviewed during each survey. The proportion of newborns dried and wrapped immediately after birth increased from 14% in 2002 to 55% in 2004; 76.2% of the newborns were put to the mother's breast within one hour of birth compared to 38.6% in baseline. Newborn check-up within 24 hours of delivery increased from 14.4% in 2002 to 27.3% in 2004. Postnatal check-up of mothers by trained providers within three days of delivery rose from 2.4% in 2002 to 27.3% in 2004. Knowledge of the mothers on at least two postnatal danger signs increased by 17.2%, i.e. from 47.1% in 2002 to 64.3% in 2004. Knowledge of mothers on at least three postnatal danger signs also showed an increase of 16%. Essential newborn-care practices, such as drying and wrapping the baby immediately after birth, initiation of breastmilk within one hour of birth, and early postnatal newborn check-up, improved in the intervention areas. Increased community awareness helped improve maternal and newborn-care practices at the household level. Lessons learnt from implementation revealed that door-to-door visits by community health workers, using community registers as job-aids, were effective in identifying pregnant women and following them through pregnancy to the postnatal periods

Design and Analysis of High Speed Multiply and Accumulation Unit for Digital Signal Processing Applications

Unit for Digital Signal Processing Applications   Kausar Jahan1, Pala Kalyani2, V Satya Sai3, GRK Prasad4, Syed Inthiyaz5, Sk Hasane Ahammad6 1Department of ECE, Dadi Institute of Engineering and Technology Anakapalle, Andhra Pradesh, India 2Department of ECE, Vardhaman College of Engineering Kacharam, Shamshabad, India 3Department of ECE, Koneru Lakshmaiah Education Foundation Guntur, India-522502 4Department of ECE, Koneru Lakshmaiah Education Foundation Guntur, India-522502 5Department of ECE, Koneru Lakshmaiah Education Foundation Guntur, India-522502 6Department of ECE, Koneru Lakshmaiah Education Foundation Guntur, India-522502   Abstract—The fundamental component used in many of the Digital signal Processing (DSP) applications are Multiply and Accumulation Unit (MAC). In the literature, a multiplier consists of greater number of full adders and half adder in partial product reduction stage, which increases the hardware complexity and critical path delay to MAC unit. To overcome this problem, two novel multipliers are proposed in this article. The proposed multipliers are designed and implemented in hardware, which reduces the circuit complexity and improves the overall performance of the MAC unit with less delay. The proposed multipliers are compared with the 4-bit existing designs and observed that the number of slices Look Up Tables (LUTs) are minimized from 113 to 43, Slices are reduced from 46 to 14, Full Adders (FAs) are lessened from 28 to 23, bonded Input Output Blocks (IOBs) and Half Adders (HAs) were not altered. The time delay is reduced from 14.251ns to 7.876ns. The proposed multipliers are compared in the literature with the 8-bit multiplier, then the number of Slice LUTs are reduced from 510 to 231, Slices are reduced from 218 to 113, FAs are reduced from 120 to 110, HAs are reduced from 56 to 39, time delay is reduced from 26.228ns to12.748ns, but bonded IOBs count remains same. The synthesis and simulations results are verified by using Xilinx ISE 14.7 version tool

Hybrid Autonomous Vehicle (Aerial and Grounded)

This work discusses hybrid autonomous vehicles that are grounded and aerial vehicles that are utilized to select their course based on their environmental characteristics. It includes algorithms for path planning, obstacle avoidance, and trajectory planning. It also has a microcontroller, known as the PIXHAWK Flight Controller, for various transmissions and configurations. Calibration and testing are performed using Mission Planner software. This article shows the different problematic features of an autonomous vehicle with several functionalities

Immediate and Early Postnatal Care for Mothers and Newborns in Rural Bangladesh

The study evaluated the impact of essential newborn-care interventions at the household level in the Saving Newborn Lives project areas. Two household surveys were conducted following the 30-cluster sampling method using a structured questionnaire in 2002 (baseline) and 2004 (endline) respectively. In total, 3,325 mothers with children aged less than one year in baseline and 3,110 mothers in endline from 10 sub-districts were interviewed during each survey. The proportion of newborns dried and wrapped immediately after birth increased from 14% in 2002 to 55% in 2004; 76.2% of the newborns were put to the mother\u2019s breast within one hour of birth compared to 38.6% in baseline. Newborn check-up within 24 hours of delivery increased from 14.4% in 2002 to 27.3% in 2004. Postnatal check-up of mothers by trained providers within three days of delivery rose from 2.4% in 2002 to 27.3% in 2004. Knowledge of the mothers on at least two postnatal danger signs increased by 17.2%, i.e. from 47.1% in 2002 to 64.3% in 2004. Knowledge of mothers on at least three postnatal danger signs also showed an increase of 16%. Essential newborn-care practices, such as drying and wrapping the baby immediately after birth, initiation of breastmilk within one hour of birth, and early postnatal newborn check-up, improved in the intervention areas. Increased community awareness helped improve mater\uadnal and newborn-care practices at the household level. Lessons learnt from implementation revealed that door-to-door visits by community health workers, using community registers as job-aids, were effective in identifying pregnant women and following them through pregnancy to the postnatal periods

Breast tumor prediction and feature importance score finding using machine learning algorithms

The subject matter of this study is breast tumor prediction and feature importance score finding using machine learning algorithms. The goal of this study was to develop an accurate predictive model for identifying breast tumors and determining the importance of various features in the prediction process.  The tasks undertaken included collecting and preprocessing the Wisconsin Breast Cancer original dataset (WBCD). Dividing the dataset into training and testing sets, training using machine learning algorithms such as Random Forest, Decision Tree (DT), Logistic Regression, Multi-Layer Perceptron, Gradient Boosting Classifier, Gradient Boosting Classifier (GBC), and K-Nearest Neighbors, evaluating the models using performance metrics, and calculating feature importance scores. The methods used involve data collection, preprocessing, model training, and evaluation. The outcomes showed that the Random Forest model is the most reliable predictor with 98.56 % accuracy. A total of 699 instances were found, and 461 instances were reached using data optimization methods. In addition, we ranked the top features from the dataset by feature importance scores to determine how they affect the classification models. Furthermore, it was subjected to a 10-fold cross-validation process for performance analysis and comparison. The conclusions drawn from this study highlight the effectiveness of machine learning algorithms in breast tumor prediction, achieving high accuracy and robust performance metrics. In addition, the analysis of feature importance scores provides valuable insights into the key indicators of breast cancer development. These findings contribute to the field of breast cancer diagnosis and prediction by enhancing early detection and personalized treatment strategies and improving patient outcomes

Interactions in vivo between the Vif protein of HIV-1 and the precursor (Pr55GAG) of the virion nucleocapsid proteins

The abnormality of viral core structure seen in vif-defective HIV-1 grown in PBMCs has suggested a role for Vif in viral morphogenesis. Using an in vivo mammalian two-hybrid assay, the interaction between Vif and the precursor (Pr55GAG) of the virion nucleocapsid proteins has been analysed. This revealed the amino-terminal (aa 1–22) and central (aa 70–100) regions of Vif to be essential for its interaction with Pr55GAG, but deletion of the carboxy-terminal (aa 158–192) region of the protein had only a minor effect on its interaction. Initial deletion studies carried out on Pr55GAG showed that a 35-amino-acid region of the protein bridging the MA(p17)–CA(p24) junction was essential for its ability to interact with Vif. Site-directed mutagenesis of a conserved tryptophan (Trp21) near the amino terminus of Vif showed it to be important for the interaction with Pr55GAG. By contrast, mutagenesis of the highly conserved YLAL residues forming part of the BC-box motif, shown to be important in Vif promoting degradation of APOBEC3G/3F, had little or no effect on the Vif–Pr55GAG interaction

Silymarin Targets β-Catenin Signaling in Blocking Migration/Invasion of Human Melanoma Cells

Metastatic melanoma is a leading cause of death from skin diseases, and is often associated with activation of Wnt/β-catenin signaling pathway. We have examined the inhibitory effect of silymarin, a plant flavanoid from Silybum marianum, on cell migration of metastasis-specific human melanoma cell lines (A375 and Hs294t) and assessed whether Wnt/β-catenin signaling is the target of silymarin. Using an in vitro invasion assay, we found that treatment of human melanoma cell lines with silymarin resulted in concentration-dependent inhibition of cell migration, which was associated with accumulation of cytosolic β-catenin, while reducing the nuclear accumulation of β-catenin (i.e., β-catenin inactivation) and reducing the levels of matrix metalloproteinase (MMP) -2 and MMP-9 which are the down-stream targets of β-catenin. Silymarin enhanced: (i) the levels of casein kinase 1α, glycogen synthase kinase-3β and phosphorylated-β-catenin on critical residues Ser45, Ser33/37 and Thr41, and (ii) the binding of β-transducin repeat-containing proteins (β-TrCP) with phospho forms of β-catenin in melanoma cells. These events play important roles in degradation or inactivation of β-catenin. To verify whether β-catenin is a potent molecular target of silymarin, the effect of silymarin was determined on β-catenin-activated (Mel 1241) and β-catenin-inactivated (Mel 1011) melanoma cells. Treatment of Mel 1241 cells with silymarin or FH535, an inhibitor of Wnt/β-catenin pathway, significantly inhibited cell migration of Mel 1241 cells, which was associated with the elevated levels of casein kinase 1α and glycogen synthase kinase-3β, and decreased accumulation of nuclear β-catenin and inhibition of MMP-2 and MMP-9 levels. However, this effect of silymarin and FH535 was not found in Mel 1011 melanoma cells. These results indicate for the first time that silymarin inhibits melanoma cell migration by targeting β-catenin signaling pathway

Precision cardiodiet: transforming cardiac care with artificial intelligence-driven dietary recommendations

The subject matter of this research revolves around addressing the escalating global health threat posed by cardiovascular diseases, which have become a leading cause of mortality in recent times. The goal of this study was to develop a comprehensive diet recommendation system tailored explicitly for cardiac patients. The primary task of this study is to assist both medical practitioners and patients in developing effective dietary strategies to counter heart-related ailments. To achieve this goal, this study leverages the capabilities of machine learning (ML) to extract valuable insights from extensive datasets. This approach involves creating a sophisticated diet recommendation framework using diverse ML techniques. These techniques are meticulously applied to analyze data and identify optimal dietary choices for individuals with cardiac concerns. In pursuit of actionable dietary recommendations, classification algorithms are employed instead of clustering. These algorithms categorize foods as "heart-healthy" or "not heart-healthy," aligned with cardiac patients’ specific needs. In addition, this study delves into the intricate dynamics between different food items, exploring interactions such as the effects of combining protein- and carbohydrate-rich diets. This exploration serves as a focal point for in-depth data mining, offering nuanced perspectives on dietary patterns and their impact on heart health. The method used central to the diet recommendation system is the implementation of the Neural Random Forest algorithm, which serves as the cornerstone for generating tailored dietary suggestions. To ensure the system’s robustness and accuracy, a comparative assessment involving other prominent ML algorithms—namely Random Forest, Naïve Bayes, Support Vector Machine, and Decision Tree, was conducted. The results of this analysis underscore the superiority of the proposed -based system, demonstrating higher overall accuracy in delivering precise dietary recommendations compared with its counterparts. In conclusion, this study introduces an advanced diet recommendation system using ML, with the potential to notably reduce cardiac disease risk. By providing evidence-based dietary guidance, the system benefits both healthcare professionals and patients, showcasing the transformative capacity of ML in healthcare. This study underscores the significance of meticulous data analysis in refining dietary decisions for individuals with cardiac conditions

Role of Nanoparticles in Environmental Remediation: An Insight into Heavy Metal Pollution from Dentistry

Environmental damage is without a doubt one of the most serious issues confronting society today. As dental professionals, we must recognize that some of the procedures and techniques we have been using may pose environmental risks. The usage and discharge of heavy metals from dental set-ups pollute the environment and pose a serious threat to the ecosystem. Due to the exclusive properties of nanosized particles, nanotechnology is a booming field that is being extensively studied for the remediation of pollutants. Given that the nanoparticles have a high surface area to volume ratio and significantly greater reactivity, they have been greatly considered for environmental remediation. This review aims at identifying the heavy metal sources and their environmental impact in dentistry and provides insights into the usage of nanoparticles in environmental remediation. Although the literature on various functions of inorganic nanoparticles in environmental remediation was reviewed, the research is still confined to laboratory set-ups and there is a need for more studies on the usage of nanoparticles in environmental remediation.</jats:p

Cellular Automata Applications in Shortest Path Problem

Cellular Automata (CAs) are computational models that can capture the essential features of systems in which global behavior emerges from the collective effect of simple components, which interact locally. During the last decades, CAs have been extensively used for mimicking several natural processes and systems to find fine solutions in many complex hard to solve computer science and engineering problems. Among them, the shortest path problem is one of the most pronounced and highly studied problems that scientists have been trying to tackle by using a plethora of methodologies and even unconventional approaches. The proposed solutions are mainly justified by their ability to provide a correct solution in a better time complexity than the renowned Dijkstra's algorithm. Although there is a wide variety regarding the algorithmic complexity of the algorithms suggested, spanning from simplistic graph traversal algorithms to complex nature inspired and bio-mimicking algorithms, in this chapter we focus on the successful application of CAs to shortest path problem as found in various diverse disciplines like computer science, swarm robotics, computer networks, decision science and biomimicking of biological organisms' behaviour. In particular, an introduction on the first CA-based algorithm tackling the shortest path problem is provided in detail. After the short presentation of shortest path algorithms arriving from the relaxization of the CAs principles, the application of the CA-based shortest path definition on the coordinated motion of swarm robotics is also introduced. Moreover, the CA based application of shortest path finding in computer networks is presented in brief. Finally, a CA that models exactly the behavior of a biological organism, namely the Physarum's behavior, finding the minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From software to wetware. Springer, 201