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Migration and embolization of a deployed stent is a rare complication of percutaneous coronary interventions (PCI) and can result in serious and potentially life-threatening complications. There are many reports of intracoronary stent entrapment, stripping, and dislodgement during PCI, however, only a few reports about migration. We report a rare case of migration of the left main coronary stent into the aortic root, which happened 5 months after the procedure and was treated by its partial removal through aortotomy along with surgical revascularization. The patient was discharged 5 days later, after an uneventful hospital stay. [ABSTRACT FROM AUTHOR]

Objectives: To find out the frequency of Electrolytes derangement (increase or decrease in serum sodium, potassium and chloride level) in patients who underwent TURP for enlarged prostate. Materials and Methods: This was a cross sectional descriptive study from 11-02-2020 to 11-08-2020 on 142 patients. After taking approval from ethical committee of the hospital. Informed consent was taken from the patients by explaining the patients. Detail physical examinations were performed and all necessary investigation was done. Preoperatively serum electrolyte levels were noted. Results: In this study, 142 patients with patient's benign prostate enlargement undergoing TURP had observed, in which 83(58.5%) patients have prostate volume have less than or equal to 15gm and 59(41.5%) patients have more than 15gm. Electrolyte derangement among patients presenting with benign prostatic enlargement after TURP was 69(48.59%). Conclusion: Electrolyte serum levels were decreased after TURP procedure. Pre and post-surgery electrolyte should be fully monitored to prevent TURP complications. [ABSTRACT FROM AUTHOR]

Cancer is one of the leading causes of death worldwide, and its incidence and mortality are increasing each year. Improved therapeutic strategies against cancer have progressed, but remain insufficient to invert this trend. Along with several other risk factors, abnormal genetic and epigenetic regulations play a critical role in the initiation of cellular transformation, as well as tumorigenesis. The epigenetic regulator UHRF1 (ubiquitin-like, containing PHD and RING finger domains 1) is a multidomain protein with oncogenic abilities overexpressed in most cancers. Through the coordination of its multiple domains and other epigenetic key players, UHRF1 regulates DNA methylation and histone modifications. This well-coordinated dialogue leads to the silencing of tumor-suppressor genes (TSGs) and facilitates tumor cells' resistance toward anticancer drugs, ultimately promoting apoptosis escape and uncontrolled proliferation. Several studies have shown that the downregulation of UHRF1 with natural compounds in tumor cells induces the reactivation of various TSGs, inhibits cell growth, and promotes apoptosis. In this review, we discuss the underlying mechanisms and the potential of various natural and synthetic compounds that can inhibit/minimize UHRF1's oncogenic activities and/or its expression. [ABSTRACT FROM AUTHOR]

Here we describe the antimicrobial potential of secondary metabolites, fulvic acid (F.A.) and anhydrofulvic acid (AFA), produced by RDE147, an endophyte of Rosa damascena Mill. The endophyte was identified as Cercospora piaropi by ITS and β-tubulin–based phylogenetic analyses, while chemoprofiling of the endophyte by column chromatography and spectroscopy yielded two pure compounds, F.A. and AFA. The compounds demonstrated different antimicrobial profiles, with AFA suppressing the growth of C. albicans at 7.3 µg ml−1 IC50. Further studies revealed that AFA strongly restricted the biofilm production and hyphae formation in C. albicans by down-regulating several biofilm and morphogenesis-related genes. The time-kill assays confirmed the fungicidal activity of AFA against C. albicans, killing 83.6% of the pathogen cells in 24 h at the MIC concentration, and the post-antibiotic effect (PAE) experiments established the suppression of C. albicans growth for extended time periods. The compound acted synergistically with amphotericin B and nystatin and reduced ergosterol biosynthesis by the pathogen, confirmed by ergosterol estimation and comparative expression profiling of selected genes and molecular docking of AFA with C. albicans squalene epoxidase. AFA also suppressed the expression of several other virulence genes of the fungal pathogen. The study determines the anti-C. albicans potential of AFA and its impact on the biology of the pathogen. It also indicates that Cercospora species may yield potential bioactive molecules, especially fulvic acid derivatives. However, it is imperative to conduct in vivo studies to explore this molecule's therapeutic potential further. [ABSTRACT FROM AUTHOR]

Introduction: Cerebral palsy (CP) is a non-progressive disorder of motor and posture with associated delayed development in areas of cognition, perception, behaviour, and sensation, due to insult to the immature brain. Improvement in all developmental domains of CP patients depends upon the early institution of rehabilitation programs involving all multidisciplinary approaches. The portage early education program (PEEP) is a worldwide, effective, and early interventional method, for developmentally delayed children. Objective: To determine the outcome of developmental therapy in children with CP by using PEEP. Study Design: Experimental research design (within the group) Settings: The Children’s Hospital and the Institute of child health Multan. Methodology: 156 patients of 2 to 10 years of age, both gender from January 2020 to December 2020, diagnosed as CP were enrolled for this study. All children were assessed by using PEEP and GMFM (Gross Motor Function Measure) twice 6 months apart. A trained clinical psychologist administered PEEP to all the children. All the Patients were advised regular weekly sessions at the hospital by a multidisciplinary team and at least 2hours per day for continuation of these therapies at home. At the end of 6 months, we found that all families had not followed this. The patient who visited > 2 times/month were considered compliant and those who visited < 2 times/month were considered non-compliant. Data were analysed by using SSPS version 16. Paired t-test was used to compare the developmental outcome of compliant and non-compliant groups. Results: Out of 156 participants, 58(37%) were in compliant, and 98(63%) were non-compliant. Male subjects were prominent 42(72.41%) and 67(68.37%) respectively. Most of the participants belonged between 3-6 years of age in both groups28(48.29%) and 51(52.04%) respectively. Spastic quadriplegia (26(44.83%), 57 (58.16%)), and level 5 on GMFM 24(42.86) and 52(53.06%) respectively were common presents in both patient groups. The mean comparison of DQ of Compliant CP Children at the initial stage and after six months in areas of GM, C, SH, S, L, and GMFM was found statistically significant. Conclusion: CP children should be on regular follow-up for developmental therapy for a long time for better outcomes. PEEP is an excellent system for the assessment, training, and rehabilitation of children with delayed development. [ABSTRACT FROM AUTHOR]

Acquired pseudoaneurysms of the aortic root involving the sinus of Valsalva (SOV) are rare and serious complications arising from trauma, infection, or following cardiac surgery or intervention. Complete heart block (CHB) is an atypical presentation of SOV pseudoaneurysm due to either direct compression effects or involvement of the main conducting system by blood and inflammatory cell infiltration. Herein, we describe a rare case of a patient who presented with CHB caused by an SOV pseudoaneurysm following polytrauma and was treated with surgical closure of pseudoaneurysm followed by implantation of a permanent pacemaker to treat the persistent CHB. [ABSTRACT FROM AUTHOR]

The study was undertaken to unravel the culturable endophytic bacterial microbiome of Crocus sativus L. (saffron crocus) and consequently obtain potential leads to develop plant growth-promoting and biocontrol agents for increased productivity and sustainable cultivation. The endophytes formed 47 different operational taxonomic units (OTUs), spanning over 28 genera. The host was preferentially colonized by the genus Bacillus, followed by Burkholderia and Pantoea, respectively. Several endophytes possessed potential plant growth-promoting properties and inhibitory activities against the specific fungal pathogens of saffron. The endophytes, except for Microbacterium oxydans, did not cause any disease symptoms in the pot experiments. The selected cultures, Burkholderia gladioli, Streptomyces achromogenes, and three species of Bacillus, enhanced the host plant growth significantly. Based on the pot experiment results, two isolates, Bacillus mojavensis CS4EB32 and Burkholderia gladioli E39CS3, were selected for the field experiments. We obtained an increase of 67.5%, 69.8%, and 68.3% in the production of flowers with the individual and collective treatments, respectively. The treatments also enhanced the biomass of the plant and the length and weight of stigmas significantly. The endophyte treatments induced the expression of the pathway genes, resulting in a marked increase in the concentration of apocarotenoids. The study indicates that the dominant endophytes support plant growth and development in nature and present an opportunity for developing microbial formulations for the sustainability of saffron cultivation. [ABSTRACT FROM AUTHOR]

Virtual power plants possess the capability to effectively manage the inherent conflict between dispersed electricity generation and user demand, hence optimising market advantages. Nevertheless, the presence of severe weather conditions, geopolitical factors, the phenomenon of global warming, and several other global concerns greatly enhance the level of uncertainty related to the establishment of sustainable energy sources and the corresponding demand from users. Consequently, these factors expose the limitations of virtual power plants in effectively controlling power supply. The active involvement of community members has the potential to invigorate the process of development. This study presents a novel model, referred to as the CE-based cVPP (CE-cVPP) model, that combines the principles of circular economy (CE) and community-based virtual power plant (cVPP). The objective of this model is to enhance the effectiveness of sustainable development efforts. This research redefines the concept of a CE-cVPP, elucidates its defining features, scopes, and classification, and constructs a technological framework, together with an explanatory community case structure. The CE-cVPP energy life cycle framework is constructed based on the principles of the circular economy paradigm. This study investigates the enduring and sustainable benefits of the CE-cVPP across four dimensions: energy, environment, economics, and society. In conclusion, this paper provides a comprehensive examination of the energy, digital, and circular economy perspectives, encompassing critical analysis, problems, prospective analysis, and future directions. • Circular economy and community-based Virtual Power Plant (CE-cVPP) is a paradigm for the future energy sector. • CE-cVPP is a novel sustainable pattern that combines circular economy and community virtual power plants. • CE-cVPP features renewable energy, electricity, gas, cold, heat, and biodegradable waste. • CE-cVPP promotes economic, social, and ecological sustainability. • CE-cVPPs enhance the power and energy system's adaptability, versatility, and stability. [ABSTRACT FROM AUTHOR]

Aim: To investigate the role of the leading saffron endophyte Burkholderia gladioli strain E39CS3 (BG‐E39) in the inhibition of corm‐rot and induced systemic resistance (ISR) in the host against the saffron specific pathogen, Fusarium oxysporum. Methods and Results: We studied the interaction between BG‐E39 and the corm‐rot pathogen F. oxysporum in vitro and in vivo. BG‐E39 strongly inhibited both the F. oxysporum strains and other saffron‐specific and non‐specific pathogens used in this study. Confrontation and microscopic analyses revealed that the endophyte possessed fungicidal activity against the pathogens and effectively induced cell death in the mycelia. The endophyte produced chitinases as well as β‐1,3‐glucanase that may be involved in the pathogen cell wall degradation. BG‐E39 did not cause corm‐rot in Crocus sativus and the closely related plant, Gladiolus, thus establishing that it is non‐pathogenic to these plants. The endophyte reduced corm‐rot through antibiosis and enhanced the endogenous jasmonic acid (JA) levels and expression of JA‐regulated and other plant defence genes. Conclusions: The bacterial endophyte BG‐E39 provides resistance to the host plant against F. oxysporum corm‐rot in nature. Significance and Impact of the Study: The current study discovers the role of the saffron endophyte BG‐E39 in providing resistance to the host against corm‐rot. Therefore, this endophyte is a potential candidate for developing a microbial formulation for the biocontrol of the most common disease of C. sativus. [ABSTRACT FROM AUTHOR]

On-grid and off-grid renewable energy sources have emerged as a more efficient way to meet large-scale urban and rural needs. The integration of renewables into the utilities/island mode as well as into the techno-economic feasibility studies determines the financial viability, technical and operational viability of the project. This study was conducted to integrate renewables and techno-economic feasibility analysis using the utility grid-connected and islanded mode to meet the demand for community and commercial loads with these strategic objectives. Three main analytical tasks have been performed, including: optimization, simulation, and sensitivity analysis. We selected four sensitivity cases and further divided each sensitivity case into four sensitivity loops, including different combinations of renewable energy sources, e.g., photovoltaics, wind turbines, battery banks, power grids, converters, and diesel generators. The following parameters were used to conduct a techno-economic feasibility analysis: capital costs, replacement costs, operating and maintenance costs, salvage costs, fuel costs, real-time commercial/community load, and climate data. The best results of sensitivity case-2 were selected for the techno-economic feasibility analysis of the remaining four sensitivity cases. The islanded mode net present and annualized sensitivity loop-1 costs in sensitivity case-2 were noted at $1381080.42 and $106832.62, respectively. Net present and annualized sensitivity loop-1 costs in sensitivity case-2 of the grid-connected utility mode were noted at $512792.1 and $39,666.71, respectively. The proposed utility grid-connected mode cost is two times lower than that of the islanded mode, so we have chosen sensitive case-2 for hourly, seasonal, and annual electrical load flow and techno-economic feasibility analysis to meet the community and commercial load demand. [Display omitted] [ABSTRACT FROM AUTHOR]

Background Recently many new deep learning–based variant-calling methods like DeepVariant have emerged as more accurate compared with conventional variant-calling algorithms such as GATK HaplotypeCaller, Sterlka2, and Freebayes albeit at higher computational costs. Therefore, there is a need for more scalable and higher performance workflows of these deep learning methods. Almost all existing cluster-scaled variant-calling workflows that use Apache Spark/Hadoop as big data frameworks loosely integrate existing single-node pre-processing and variant-calling applications. Using Apache Spark just for distributing/scheduling data among loosely coupled applications or using I/O-based storage for storing the output of intermediate applications does not exploit the full benefit of Apache Spark in-memory processing. To achieve this, we propose a native Spark-based workflow that uses Python and Apache Arrow to enable efficient transfer of data between different workflow stages. This benefits from the ease of programmability of Python and the high efficiency of Arrow's columnar in-memory data transformations. Results Here we present a scalable, parallel, and efficient implementation of next-generation sequencing data pre-processing and variant-calling workflows. Our design tightly integrates most pre-processing workflow stages, using Spark built-in functions to sort reads by coordinates and mark duplicates efficiently. Our approach outperforms state-of-the-art implementations by >2 times for the pre-processing stages, creating a scalable and high-performance solution for DeepVariant for both CPU-only and CPU + GPU clusters. Conclusions We show the feasibility and easy scalability of our approach to achieve high performance and efficient resource utilization for variant-calling analysis on high-performance computing clusters using the standardized Apache Arrow data representations. All codes, scripts, and configurations used to run our implementations are publicly available and open sourced; see  https://github.com/abs-tudelft/variant-calling-at-scale. [ABSTRACT FROM AUTHOR]

Fall armyworm, Spodoptera frugiperda belongs to order Lepidoptera and family Noctuidae, is becoming a serious threat in maize growing areas of the world. Firstly, it was reported from African countries and caused significant economic crop losses. Recently the pest has been reported from various areas of Pakistan and knowledge of biomorphic characters was important. By keeping in view, the current study was conducted. The study resulted that complete metamorphosis was observed. The eggs were white to yellow in colour and dome in shape with 2-3 days of the incubation period. The egg width and thickness were recorded as 0.22 and 0.19 mm, respectively. Six larval instars were recorded. The pinacula of each seta was well recorded on all larval instar but more conspicuous in the last instar (6th) as compared to others. Inverted Y line was present on larvae head that mostly differentiates this species from other reported Spodoptera species. First, second, third, fourth, fifth and sixth larval head capsule width was 0.18±0.02, 0.29±0.00, 0.50±0.01, 0.76±0.47, 1.10±0.13 and 1.98±0.12 mm, respectively. The length and width of the 6th instar larva were 26.98±2.93 and 3.90±0.00 mm, respectively. The total larval period was 15-20 days on maize leaves. Obtect pupa was whitish green. Male was short-lived than female. The pre oviposition, oviposition and post oviposition periods were 2-5, 2-4 and 4-6 days, respectively. The study will be proved fruitful for coming researchers and farmers to control this pest. [ABSTRACT FROM AUTHOR]

The classical necroptosis signaling is mediated by death receptors (DRs) that work in synergy with traditional caspase inhibitory signals. Currently, potential therapeutic molecules are in various phases of clinical trials for a spectrum of pathological conditions associated with necroptosis. However, a non-classical model of necroptosis has also emerged over the last decade with a relatively unexplored molecular mechanism. Although in vitro studies and preclinical models have shown its close association with mitochondrial dysfunction (mito-dysfunction), contradictory reports have emerged which complicate its definitiveness. Though impaired mitochondrial calcium ([Ca2+] m) handling is established in necrotic cell death, how this interplay regulates necroptosis is yet to be elucidated. Taking these questions into consideration, we have discussed various molecular aspects of necroptosis with the emerging role of mito-dysfunction. Based on the central role of altered [Ca2+] m handling in mito-dysfunction mediated necroptosis, we have provided a comprehensive molecular insight into this emerging paradigm. Potential reasons for the contradictory findings regarding the role of mito-dysfunction in necroptosis in general and mitochondrial-dependent necroptosis in specific are discussed. We also provide insights into the current understanding of how [Ca2+] m can be a critical determinant in deciding the cell fate under certain pathological conditions, while under others it may be dispensable. Lastly, we have highlighted the key molecular targets which have a direct implication for therapeutic intervention in conditions that are associated with impaired [Ca2+] m handling and cell death by necroptosis. [ABSTRACT FROM AUTHOR]

Background: Subcutaneous emphysema (SE) is a clinical manifestation of underlying pathology. Identification and addressing those etiologies are therefore important for prevention and management. Objective: To evaluate the causes and management outcome of SE in Thoracic Surgery unit, JPMC, Karachi. Methods: A prospective, observational study was conducted from 1st February 2019 till 31st January 2020. Patients aged 12 years or more of either gender were included. Patient's demographics, clinical information, causes and treatment provided were recorded. Outcome were noted in terms of days required for resolution of SE along with mortality. Results: A total of 155 patients with SE were included. Most common cause was iatrogenic in 58 (37.4%) followed by blunt chest trauma in 44 (28.4%). Secondary spontaneous pneumothorax, penetrating chest trauma, blunt neck trauma, primary spontaneous pneumothorax, and penetrating neck trauma were the other causes. Iatrogenic causes included 21 (36.2%) with chest tube malfunction, 14 (24.1%) secondary to barotrauma. All patients had complete resolution with treatment. Extensive SE observed in 28 (18.1%) patients and was significantly associated with iatrogenic causes (P <0.005). Iatrogenic cases took longer days for clinical resolution (P<0.005) and radiological resolution (P< 0.05). There was no mortality primarily due to SE. Conclusion: Trauma to the lungs and airways secondary to iatrogenic injury is an important preventable cause of SE. Severity of emphysema tend to be extensive among iatrogenic injuries thus taking longer days for resolution. Successful management is possible with high flow oxygen, bronchodilation and meticulous handling of chest tubes. [ABSTRACT FROM AUTHOR]

A field study was conducted to assess the potential role of phosphorus (P) on growth and yield of Phalsa (Grewia asiatica L.). Healthy phalsa plants were fertilized with P2O5 @ 50g, 100g and 150g plant-1. Data were recorded on number of fruit bush-1, yield bush-1 (kg), single fruit weight (g), fruit diameter (cm), number of fruiting nodes, length of new shoot (cm), number of sprouted shoots cane-1 and leaf area (cm2). Treatments were applied under Randomized Complete Block Design (RCBD) in triplicates and means were compared by Tukey's HSD test. Association of the biological traits of phalsa with phosphorus was further estimated by correlation analysis. Results indicated that phosphorus application @ 150g plant-1 has improved various traits i.e. number of fruit bush-1 (4866.7), yield bush-1 (2.19 kg), single fruit weight (0.656 g), number of fruiting nodes (12.18) and number of sprouted shoots cane-1 (13.89). However, vegetative characters particularly leaf area showed negative correlation (-0.264) with the increased amount of phosphorus. Fruit diameter (p=0.993) and length of new shoot (p=0.122) did not exhibited any correlation with applied phosphorus. Overall it is concluded that phosphorus is not only helpful but necessarily required for fruit quality and optimum yield of phalsa. [ABSTRACT FROM AUTHOR]

Cryptocurrencies have been historically characterised by large price swings and inherent volatility at a much higher scale than traditional financial assets. Understanding the underlying mechanisms and whether, or how, these are priced in through possible risk premia is crucial to bringing cryptocurrencies closer to mainstream financial markets. Using data on 1982 cryptocurrencies form January 1, 2015 till September 30, 2020 and a combination of models involving portfolio-level and Fama–MacBeth analyses, while accounting for cryptocurrency sample selection, we show that the additional risk measured by idiosyncratic volatility is well priced in cryptocurrencies and investors are being paid a risk premium for their holdings. However, a deeper inspection of the dynamics reveals that such a trade-off is mostly valid for the most illiquid cryptocurrencies, which are susceptible to microstructure noise. [ABSTRACT FROM AUTHOR]

Highlights • AI-based models are proposed for utility district-level load management. • Two different climate zones actual energy consumption and climate data is used. • The potentials of PRGBNNs & GDALBNNs models is investigated at five different PS states. • AI-based models show several benefits in forecasting accuracy and model development. • AI-based models compared with three existing model & show accurate and better performance. Abstract Increasing sustainability demands initiate estimating various design and control opportunities for classifying energy-efficient plan ever more significant. These conditions demand simulation algorithms which are not only fast, but also accurate. Artificial intelligence (AI) enables efficient mimicry of bulk energy consumption control while producing results much faster than data-mining and machine learning models. This study proposes two AI based approaches for utilities bulk energy consumption prediction, control and management. Two different zones actual environmental and energy consumption data are obtained for input feature selection and modeling analysis. Each zone is categorized into five features parameter selection (PS) states. Each PS state is further divided into four different hidden neurons (HD) and hidden layers of the model's network. The forecasting duration is based on 1-month and 1-year ahead intervals for medium-term (MT) and long-term (LT) respectively. Further the current proposed model's performance is compared with three existing models. One of the promising findings in this research is that substantial improvement in prediction accuracy applying features extracted by PS-3 and PS-5. Results show that AI models are powerful in solving complex and nonlinear patterns of raw data. This study renders optimal decisions can be projected while utilities energy supply strategy & control, capacity expansion, capital investment research market management, revenue analysis and future load requirement forecasting. [ABSTRACT FROM AUTHOR]

Poultry birds fulfill the dietary requirements of animal proteins for humans. Raising of poultry birds is the challenging task for poultry growers especially during the harsh weather. The extreme summer season and heat stress check productive performance of poultry flocks. Heat stress affect feed intake, water intake, feed conversion ratio, growth, reproductive performance, immune response, egg quality, meat quality, the egg fertility, birds' welfare, the hormonal response and other functions. Efficient role of farm management can play its role in combating the heat stress through management of feeding, watering, litter and stocking density. The nutritional supplements of vitamins, amino acids and minerals play role in averting heat stress in poultry flocks. The present paper overviews the effects of heat stress on health of poultry birds and measures for averting heat stress in poultry flocks. [ABSTRACT FROM AUTHOR]

Abstract Short-term load prediction at the district-level is essential for feeders, substations, consumers and transformers starts from 1-h to one-week ahead. Though, the critical problem is in the design of complex load characteristics, switching operations and a large number of junctions at electrical distribution feeders. To overcome the design challenges, this research conducted an analysis of short-term energy requirement forecasting for district-level by applying into two deep learning models. Present, the booming increase of deep learning (DL) methods give them encouraging options to data-driven techniques. However, DL gives exceptional ability in handling the non-linear complex relations, model computation performance and complexity are of attention. Further, the two different climate zones data used for modeling analysis. The input feature parameter sets classified into two parts: i) features selection-I (FS-I); and FS-II. The FS-I comprises based on seven environmental parameters, including various limits of energy consumption data sets and the FS-II contains the different sixteen feature variables. The FS-II mean absolute error is <2.05% and <1.577% of OSSB-NN and BFGS-QNB respectively. The deep learning models achieved higher forecasting accuracy at the proposed structure of the model's network at different hidden neurons. Based on the current trend and the comparison results, a structure of district energy prediction and future research direction proposed for district-level design, planning, and services. Highlights • Two deep-learning models used to forecast future load demand of two climate zones. • Models will play essential role in the future district energy forecasting. • Higher accuracy obtained for short-term horizons leads to economic advantages. • The MAPE of BFGS-QNB forecasting model is <3.679%. • A framework is deep learning proposed for design, planning, and service. [ABSTRACT FROM AUTHOR]

Background: Mitral valve procedures remain a surgical challenge in the presence of extensive annular calcification, which presents a formidable technical challenge. Aggressive debridement is limited by risk of serious complications and the technical complexity of pericardial patch reconstruction of the debrided area.Methods: An open surgical approach with a transcatheter valve allows the valve to be placed under direct visualisation to facilitate positioning and to evaluate the likelihood of both perivalvular leakage and atrioventricular disruption. The open approach has the additional advantage of performing concomitant surgeries like other valve procedures, arrhythmias surgeries and coronary bypass.Results: We present our experience with open surgical mitral valve replacement (MVR) using transcatheter valve in different patients requiring varied procedures. These patients were not suitable for MVR using standard prosthetic valve and techniques. They were also not suitable for percutaneous MVR because of heavily calcified anterior mitral leaflet and the other concomitant procedures required.Conclusions: Open MVR with a transcatheter balloon-expandable valve can avoid the need for technically challenging and high-risk decalcification of mitral annulus. These novel techniques using transcatheter valves can be successful in complex cases where standard prosthetic valves are impossible to implant in a heavily calcified mitral annulus. [ABSTRACT FROM AUTHOR]

Highlights • Three data-mining models are applied for cooling load demand forecasting. • Short and medium-term intervals are applied for future energy demand forecasting. • Environment variables effect on cooling load demand is also analyzed. • Model's performance and accuracy has been estimated at selection of different hidden neurons. • The best cooling load forecasting performance achieved at the short-term interval. Abstract In this study, data-mining techniques comprising three forecasting algorithms for accurate and precise cooling load requirement prediction in the building environment, with the primary aim and the objective of improving the load management are applied. Three state-of-the-art cooling load prediction algorithms are – multiple-linear regression (MLR) model, Gaussian process regression (GPR) model and Levenberg–Marquardt backpropagation neural network (LMB-NN) model. The Pearson correlation analysis is practiced calculating the correlation between actual cooling load demand and input feature variables of climate parameters. The impact of climate variability on the building load requirement is also analyzed. Forecasting intervals are divided into two basic parts: (i) 7-day ahead prediction; and (ii) 1-month ahead prediction. To assess the prediction performance, four performance evaluation indices are applied, which are: (i) coefficient of correlation (R); (ii) mean absolute error (MAE); (iii) mean absolute percentage error (MAPE); and (iv) coefficient of variation (CV). The model's performance is compared with the selection of different hidden neurons at different load conditions. The MAPE for 7-day ahead prediction interval by MLR, GPR and LMB-NN model is 13.053%, 0.405% and 2.592%, respectively. Furthermore, the data-mining algorithms are compared and validated with the previous study, and the MAPE of Bayesian regularization neural networks is calculated 2.515% for 7-day ahead prediction. It was witnessed that the algorithms could be applied to facilitate the building cooling load prediction, by applying a relatively limited number of parameters related to energy usage as well as environmental impact in the building environment. The forecasting results show that the three algorithms are effective in predicting the irregular behavior in the data as well as cooling load demand prediction. [ABSTRACT FROM AUTHOR]

The four rural poultry flocks were reared up-to age of 10-12th weeks during the year 2017 in district Attock (Pakistan). Mortality pattern in relation/comparison to the temperature and percent humidity was recorded. The vitamin supplements, immune boosters, preventive vaccination and prophylactic medications were given with respect to age of flock. The highest mortality was observed in the flock B at 11th week of age, followed by flock A at 1st week of age, followed by flock C at 8th week of age. The increased mortality during month of June was due to heat stress, the mortality during months of January was due to cold stress, while mortality during the month of August was due to excessive heat and high humid environment. The cold environment caused chilling of the chicks and required extra care while brooding during winter season. The hot and humid environment predisposed the chicks towards coccidiosis and the required stirring of litter and medication against coccidiosis. [ABSTRACT FROM AUTHOR]

Medium-term and long-term energy prediction is essential for the planning and operations of the smart grid eco-system. The prediction of next year and next month energy demand of grid station, independent power producers, commercial, domestic and industrial consumers are allowed administrators to optimize and plan their resources. To address the forecasting problems, the basic intention of this study is to propose an accurate and precise medium and long-term district level energy prediction models employing the machine learning based models which are: 1) artificial neural network with nonlinear autoregressive exogenous multivariable inputs model; 2) multivariate linear regression model; and 3) adaptive boosting model. Based on environmental and aggregated energy consumption data as the model's input and output, the load prediction interval is further classified into three main parts, 1-month ahead forecasting, seasonally ahead forecasting and 1-year ahead forecasting. Feature extraction, data transformation and outlier detection are performed through different data tests. The prediction results intimate that the intended models cannot only increase the forecasting accuracy contrasted with previous forecasting models but also produce adequate forecasting intervals in the smart grid environment. Additionally, these techniques describe an essential step-forward, consolidating the spatiotemporal use of energy inconstancies and variations of district level and strong forecasting capabilities of energy usage requirement in future perceptive. [ABSTRACT FROM AUTHOR]

The substantial measure of energy usage connected to the building atmosphere supports and sustains power usage modeling diligence. Amongst the numerous strategies to elaborate energy methods, supervised based machine learning approaches are immeasurable alternative to circumvent the inconvenience correlated to various engineering and data mining approaches when measured/observed data are accessible. This research depicts an analysis of electricity requirement forecasting by supervised based machine learning models with the limited data information. The power usage or energy consumption data is collected from power transmission and distribution networked organization independent system operator New England for one-year ahead energy forecasting. Moreover, energy consumption data is categorized into monthly, seasonally and yearly basis to foresee the performance for short-term, medium-term and long-term as well. Four-supervised based machine learning models employed for energy forecasting which are: i) Binary Decision Tree; ii) Compact Regression Gaussian Process; iii) Stepwise Gaussian Processes Regression; iv) Generalized Linear Regression Model. The input variables comprise the limited external environmental data, day-type/hour-type and the net energy consumption of various types of load. The output is the total energy demand of the building power usage. Modeling studies are escorted for expected energy demand in future perceptive based on Independent System Operator New England data. The performance evaluation indices applied in evaluating the model's forecasting error are coefficient of variation and mean absolute percentage error. In autumn season, the best MAPE and CV of the binary decision tree is 0.809% and 1.359% respectively for seasonal forecasting, and is 0.989% and 1.601% respectively for yearly forecasting. It is observed that the accuracy in forecasting is modest in the autumn season. In yearly prediction, the MAPE and CV of compact regression Gaussian process, stepwise Gaussian processes regression and generalized linear regression are 3.245% and 3.650%, 4.039% and 4.860%, 5.118% and 5.927% respectively. The machine learning model's performance compared and validated with the actual energy consumption, existing artificial neural network model and the mean absolute percentage error and coefficient of variation found 2.416% and 3.290% respectively for yearly prediction. It is depicted that including the utilization of limited energy usage and environmental data as one of the model's input variables, the electricity forecasting precision is more accurate, precise and can be improved. [ABSTRACT FROM AUTHOR]

Saffron (Crocus sativus L.) is one of the most expensive spices in the world due to its medicinal and aromatic value. However, saffron production is severely affected by the corm rot disease throughout the saffron producing countries. In this study, we report a basidiomycetous latent pathogen of saffron, designated as CSE26, capable of producing phytotoxic compounds. CSE26 is a highly odorous basidiomycete with monomitic hyphal system. Molecular phylogeny of ITS and 28S ribosomal gene sequence of CSE26 assigned it as Porostereum spadiceum. It was found to produce corm rot in C. sativus under in vivo and field conditions, with a disease severity index of 0.7 and 0.5, respectively. CSE26 was found to produce chlorinated aromatic compounds (CAMs) having phytotoxic activity against Arabidopsis plants. Therefore, these compounds may be acting as pathogenic determinants of CSE26. However, there is a need to study the level of production of these CAMs by this fungus in the natural environment and their effects on plant health. [ABSTRACT FROM AUTHOR]

Objective: To investigate and compare the effectiveness of tamsulosin versus standard medical therapy (SMT) for conservative management of ureteric stones in terms of time needed for stone expulsion, reduction in pain episodes and stone expulsion rates. Methodology: This was a prospective non-blinded, non-randomised controlled study conducted at Urology Unit, LRH Peshawar, from July 2015 to June 2016. 50 patients were included, 25 patients in Group 1 (SMT) and 25 patients in Group 2. All patients were selected consecutively from the outpatient department with a stone size =10 mm. In Group 1, patients were prescribed adequate oral analgesics while Group 2 patients were given tamsulosin plus analgesics for pain relief on as need basis. Data was obtained about symptoms duration, stone size, stone location, total pain episodes and the time to passage of stone. Results: There were 35 (70%) males and 15 (30%) females with a ratio of 2.3:1. Overall mean age was 31.84 ±5.64 years; range was 22 to 42 years; and mean duration of symptoms was 6.64 ±1.48 days. The overall mean stone size was 5.94 ±1.46 mm (range 4 mm to 9 mm). Significant differences were detected in total pain episodes (mean pain episodes for group 1= 3.64 & group 2= 1.6, p <0.0001) and stone passage time (mean passage time for group 1= 17.52 ±7.22 days & group2 = 10.44 ± 5.64 days, p <0.0001). Conclusions: Tamsulosin effectively reduce the time needed for spontaneous expulsion of ureteric stones. It increases the rate of stone passage and importantly reduces the number of pain episodes. [ABSTRACT FROM AUTHOR]

This paper examines the data-mining and supervised based machine learning models for predicting 1-month ahead cooling load demand of an office building, including the primitive intention of enhancing the forecasting performance and the accuracy. The data-mining and supervised based machine learning models include; regression support vector machine, Gaussian process regression, scaled conjugate gradient, tree bagger, boosted tree, bagged tree, neural network, multiple linear regression and bayesian regularization. The external climate data, hours/day in a week, previous week load, previous day load and previous 24-h average load are applied as input parameters for these models. Whereas, the output of the models is the electrical power required for water source heat pump. A water source heat pump located in Beijing, China, is selected for examining 1-month ahead cooling load forecasting, i.e., from July 8 to August 7, 2016. In this paper, simulations are classified into three sessions: 7-days, 14-days and 1-month. The forecast performance is assessed by computing four performance indices such as mean square error, mean absolute error, root mean square error and mean absolute percentage error. The mean absolute percentage error for 7-days ahead cooling load prediction of the water source heat pump from data-mining based models, Gaussian process regression, tree bagger, boosted tree, bagged tree and multiple linear regression were 0.405%, 3.544%, 1.928%, 1.703% and 13.053% respectively. While, mean absolute percentage error of 7-days ahead forecasting in case of machine learning based models such as a regression support vector machine, Bayesian regularization, scaled conjugate gradient and neural network were 12.761%, 2.314%, 6.314%, 2.592% respectively. The percentage forecasting error index proved that the results of data-mining based models are more precise and similar to the existing machine learning models. The results also demonstrate that the better performance and efficiency in foreseeing the abnormal behaviour in forecasting and future cooling load demand in the building environment. [ABSTRACT FROM AUTHOR]

Background: In children and adolescents, a Ross/Konno operation is commonly done to both enlarge the aortic root and provide a competent aortic valve with relief of left ventricular outflow tract obstruction (LVOTO). Optimum management is not so straightforward in adults.Methods: Between 1995 and 2014, 16 patients of mean age 39.4 years (18-57 years) with hypoplastic aortic annulus (AA) measuring 20mm and less, and mean aortic valve/LVOT gradient of 61mmHg (30-70mmHg) presented for surgery.Results: Eight patients with mean LVOT/AA diameter 19.6mm (18-20mm) underwent an "inclusion-cylinder" type Ross procedure (RP). Eight patients with more severe LVOT/AA obstruction, with mean diameter of 17.4mm (16-19mm) underwent mechanical aortic valve replacement (AVR) with standard Konno-type aortoventriculoplasty. There was zero early and late mortality; with mean follow-up of 11.6 years (3-21 years) in the Ross group and 6 years (2-10 years) in the Konno-AVR group. One patient in the Konno-AVR group had reoperation after 2 years for RVOT obstruction. The postoperative echocardiograms of these patients at last follow-up show residual mean gradient across LVOT/AA of 4.4mmHg (2-6mmHg) after RP, and 11.9mmHg (8-17mmHg) after Konno-AVR.Conclusions: In adults, the "inclusion-cylinder" Ross-procedure is a good alternative for mild to moderate aortic root hypoplasia. However, for cases with severe LVOT obstruction, a Ross-Konno is not possible with the same method of autologous support used in a non-Konno RP, and this could be expected to have an impact on late durability and the need for further intervention, in a group that has already undergone multiple procedures in childhood. Both methods of RP and Konno-AVR lead to excellent early and late results. [ABSTRACT FROM AUTHOR]

This paper depicted the novel data mining based methods that consist of six models for predicting accurate future heating and cooling load demand of water source heat pump, with the objective of enhancing the prediction accuracy and the management of future load. The proposed model was developed to ease generalization to other buildings, by making use of readily available measurements of a comparatively small number of variables related to water source heat pump operation in the building environment. The six models are - tree bagger, Gaussian process regression, multiple linear regression, bagged tree, boosted tree and neural network. The input parameter comprised the prescribed period, external climate data and the diverse load conditions of water source heat pump. The output was electrical power consumption of water source heat pump. In this study, simulations were conducted in three sessions - 7-day, 14-day and 1-month from 8th July to 7th August 2016. The forecast precisions of data mining models were measured by diverse indices. The performance indices which were used in assessing the prediction performance were - mean absolute error, coefficient of correlation, coefficient of variation, root mean square error, mean square error and mean absolute percentage error. The mean absolute percentage error results for 7-day future energy demand forecasting from tree bagger, Gaussian process regression, bagged tree, boosted tree, neural network and multiple linear regression were 3.544%, 0.405%, 1.703%, 1.928%, 2.592% and 13.053%, respectively. Moreover, when the proposed data mining model performance was compared with the existing studies, the mean absolute percentage error of 2.515% was found out for the first session, 7-day. The results also showed that the six models were efficient in foreseeing the abnormal behavior and future cooling and heating load demand in the building environment. [ABSTRACT FROM AUTHOR]

Energy consumption models play an integral part in energy management and conservation, as it pertains to buildings. It can assist in evaluating building energy efficiency, in carrying out building commissioning, and in identifying and diagnosing building system faults. This review takes an in-depth look at energy-demand prediction models for buildings in that it delves into recent developments in building energy approaches used to predict energy usage. By enlisting current approaches to the modelling of buildings, methods for building energy simulations can be categorized into four level classes as follows: (i) data-driven approaches; (ii) physics-based approaches; (iii) large scale building energy forecasting approaches; and (iv) hybrid approaches. The focus of this review is to target the data-driven approach and large-scale building energy predicting-based approaches. Here the data driven approaches can be categorized by (1) artificial neural network based approaches; (2) clustering based approaches; (3) statistical and machine learning-based approaches; and (4) support vector machine based approaches. From there, the type of data-driven based approach is further grouped by (a) benchmarking models; (b) energy-mapping models; (c) energy forecasting models; and (d) energy profiling models. Large-scale building-energy prediction techniques is then categorized as follows: (1) white-box based approaches; (2) black-box based approaches, and (3) grey-box based approaches. The current study explores first-rate data-driven based approaches about building energy analysis for industrial, commercial, domestic, etc., within a rural and urban setting. This review paper is based on the necessity of identifying points of departure and research opportunities for urban and rural-level analyses of building level energy performance. A variety of issues are explored which include: energy performance metrics; end-use of different building types; multiple levels of granularity; and urban and rural scales. Each technique encompasses a variety of input information as well as varying calculations or simulation models along with furnishing contrasting outcomes that suggest a variety of usages. A thorough review of each technique is presented in this study. This review highlights strengths, shortcomings, and purpose of the methods of numerous data-mining based approaches. A comprehensive review of energy forecasting models that are specified in the literature part is also provided. [ABSTRACT FROM AUTHOR]

Background: Early degeneration of prosthetic aortic valve in transcatheter aortic valve replacement (TAVR) is a rare complication.Method: We report the case of a 75-year-old woman who presented with severe calcific stenosis of Edwards SAPIEN-XT valve implanted only 4 years previously. She is a Jehovah's Witness and has background of Sjogren's syndrome with secondary cryoglobulinaemic vasculitis. She was not a suitable candidate for valve-in-valve TAVR in view of early prosthetic valve failure by calcification.Result: She underwent surgical explantation of the Sapiens XT valve and mechanical aortic valve replacement.Conclusion: To our knowledge, this is the first time that early TAVR valve degeneration and failure of an Edwards SAPIEN-XT valve was reported which required surgical replacement with a mechanical valve. [ABSTRACT FROM AUTHOR]

This review paper focuses on the various modeling practices for the identification and apprehension of non-technical losses. The modeling practices are extremely vital to develop, upsurge energy performance, examine and foresee the performance of power transmission & distribution of the electrical system. The data mining based models are innovative and have the subsistence to examine an enormous potential of energy consumption records and performing area profile for preparing housing zone directing the electricity effective living. In this concern, support vector machine model, which classifies illegal customers is a form of advanced mix evolutionary neural network model. Optimum-path forest clustering process is activated to recognize legitimate and irregular profiles of an industry as well as commercial customers to find out theft of electricity. Real time state estimation technique determines a state approximation method in the actual stage for every conversion (transformation) point. Aforementioned allows us to regulate the parts to the maximum extent of non-technical losses through the radial distribution method. The support vector machine with genetic algorithm advances a hybrid method for the non-technical loss investigation and provide an automated assistance to dominate the electricity theft. This model is simplified version of support vector machine. Decision tree and Bayesian regularization networks are appropriated to identify the several kinds of patterns of losses in the electrical system. These practices have been accompanied concerning testing and validation for power system losses in the experimental laboratory. It operates in an influence tool intended to expedite the investigators and scientists. It assists short of spending a massive amount of money, time and energy in experimental events. Prior fabrication modeling methods are remarkably significant in replication of diverse kinds of solar electrical systems. Accordingly, this study concentrates on the base of modeling methods not only saves time but, also preserves the monetary investment in the electrical system. The benefit and imminent opportunity of modeling practices are also conferred in the review paper. [ABSTRACT FROM AUTHOR]

The demand of natural fibres is increasing in the world for industrial uses to make high value products. An enormous potential of sisal fibre is present in Pakistan for making ropes, twines, paper, carpets, cloth and handicrafts, which has not been exploited so far. The aim of this study was to design and develop a small-scale portable sisal decorticator for extracting fibre from sisal leaves. A prototype of sisal decorticator was designed and fabricated at Agricultural and Biological Engineering Institute, National Agricultural Research Centre, Islamabad. The prototype machine was tested for fibre extraction. Results indicated that sisal fibre production capacity of this machine was 15.94 kg h-1 (dry fibre). Average yield of dry sisal fibre was 3.2% of green leaf weight and average fibre length was 1348.9 mm per leaf. Average power required to decorticate sisal leaves was 3.1 kW. Operating cost of sisal decorticator together with a combing machine was Rs. 507.9 h-1. The breakeven point or payback period of this technology was 233 h. Sisal fibre needs to be washed, dried and brushed after decortication process for getting good quality fibre to make domestic products. This technology has a great potential and needs to be commercialised in sisal growing areas of the country. [ABSTRACT FROM AUTHOR]

Objectives: To compare the efficacy of intralesional mitomycin C versus triamcinolone in reducing the recurrence of urethral stricture after internal optical urethrotomy. Materials and Methods: This randomized control trial was conducted at Institute of Kidney Diseases, Hayatabad Medical Complex Peshawar from January 2016 to January 2017. We enrolled 134 patients fulfilling the inclusion criteria. Urine R/E, blood investigation followed by x-ray KUB, ultrasound of the pelvis and abdomen once done with it then will proceed to retrograde urethrogram supplemented by Uroflometry & Flexible urethroscopy. Patient were regularly followed up on 3month interval for 1 year once the Mitomycin or triamcinolone injected intralesionally with TLA needle using straight working channel Pediatric cystoscope at 12, 3 & 9 o'clock positions. On each follow up uroflowmetry, & if needed Retrograde Urethrogram (R.U.G) was performed. On last follow up R.U.G was mandatory to rule out stricture. Results: Total 134 patients were enrolled in the study divided in to two groups Group A (Intralesinal Mitomycin) and Group B (Triamcinolone) after internal optical urethrotomy. Mean and SD of age for Group A was recorded as 47.4+14.2 and Group B was recorded as 46.58+14.27. Overall, only 29 (21.64%) patients demonstrated recurrence, whereas in the remaining 105 (78.35%) patients no recurrence of urethral stricture post procedure was documented on follow up. In Group A the recurrence was recorded as 9 (6.71%) whereas 58 (43.28%) patients had no recurrence. In Group B 20 (14.92%) patients had recurrence whereas 47 (35.07%) patients had no recurrence. P Value= 0.021. Frequency and percentage for stricture site between the two groups were recorded as 32 (23.88%) in Bulbar, 27 (20.14%) in Penile and 8 (5.79%) both Bulbar and Penile in Group A and in Group B 35 (26.11%) were recorded in Penile, 21 (15.67%) in Bulbar and 11 (8.20%) in both sites. Conclusion: Mitomycin C was found to be highly effective in preventing the recurrence of urethral stricture after IOU in comparison with triamcinolone. [ABSTRACT FROM AUTHOR]

OBJECTIVES: To evaluate the screening practices of nurses in relation to breast cancer and its risk factors. STUDY DESIGN: A cross sectional Observational study. PLACE AND DURATION: The study was conducted in the two public hospitals i.e. The Children's Hospital & the Institute of Child Health Multan and Nishtar Hospital and Medical College Multan from 1st June 2015 to 30th November 2015. METHODOLOGY: A sample of 200 female nurses was selected through convenient sampling technique. The charge nurses working on regular basis in the public sector hospitals were included and other nursing college trainees were excluded in the study. Questionnaire was used as a tool that include; frequency, percentage, mean and standard deviation. Questions regarding breast screening practices, awareness, breast self examination were asked. RESULTS: Of the 200 respondents 70.0% of the respondents had ever heard of breast self-examination while 30.0% reported that they had not ever heard about breast self-examination. Majority of the respondents 71.5% reported that they had not ever practiced breast self-examination and only 28.5% reported that they had practiced breast self-examination. About 96.0% respondents reported that they had not got their breasts ever examined by a doctor or a nurse and only 4.0% said they got examined their breasts by a doctor or a nurse. CONCLUSION: The knowledge of breast screening practices among female nurses was relatively poor and it needed to be improved. [ABSTRACT FROM AUTHOR]

Localization inWireless Sensor Networks (WSNs) has been an active topic for more than two decades. A variety of algorithms were proposed to improve the localization accuracy. However, they are either limited to two-dimensional (2D) space, or require specific sensor deployment for proper operations. In this paper, we proposed a three-dimensional (3D) localization scheme for WSNs based on the well-known parametric Loop division (PLD) algorithm. The proposed scheme localizes a sensor node in a region bounded by a network of anchor nodes. By iteratively shrinking that region towards its center point, the proposed scheme provides better localization accuracy as compared to existing schemes. Furthermore, it is cost-effective and independent of environmental irregularity. We provide an analytical framework for the proposed scheme and find its lower bound accuracy. Simulation results shows that the proposed algorithm provides an average localization accuracy of 0.89 m with a standard deviation of 1.2 m. [ABSTRACT FROM AUTHOR]

In the countries such as Pakistan, for analyzing the losses and techniques in the power distribution and for mitigating, are the two active areas of research which is spread globally for increasing the accessibility of power irrespective of installing future generation equipment. As, the Technical Loss and the Non-Technical Loss are accounted by the total energy losses. They both are also referred as TL and NTLs. In terms of the non-technical losses there are major financial losses for the utility companies present in the countries that are in the developing stage. NTLs is the major cause for the additional losses and also it includes the part of damaging the network that includes the infrastructure and network reliability reduction. This paper is subjected to investigating the non-technical losses in terms of the power distribution systems. In addition to that, the consumer energy consumption information is used for analyzing the NTLs from Rawalpindi region from the different feeder source. The data of Low Voltage (LV) of the distribution network are focused more that consists of commercial, industrial, residential and agricultural consumers by the use of KWh interval data which is captured over a month using the smart meter infrastructure. The discussion of this review paper determines analysis and prevention techniques of NTLs to safeguard from the illegal use of the electricity in the distribution of electrical power system. [ABSTRACT FROM AUTHOR]

Wheat straw chopper is a resource conservation technology that makes chaff from combine-harvested wheat straw and saves the environment from smoke pollution, if straw is burnt in the field. Field performance of this technology has not been evaluated yet, although this technology has been adopted in Pakistan for many years. The objective of this study was to evaluate the performance of an improved version of wheat straw chopper in combine-harvested wheat fields of southern Punjab and to determine the economics of this machine. Five test plots ranging from 0.39 to 0.61 ha were selected to determine the performance of this machine. Results revealed that a 75 hp tractor was suitable for operating this machine. Average operating speed of chopper was 2.7 km h-1. Average effective field capacity of chopper was 0.40 ha h-1 and field efficiency was 67.9%. Amount of chaff recovered was 2404 kg ha-1 and chaff recovery from straw was 61.1%. The operating cost of chopper setup was Rs. 5,262 ha-1. Total worth of recovered chaff was Rs. 24042 ha-1 (@ Rs. 10 kg-1). Net gain in terms of recovered wheat straw was Rs. 18780 ha-1. The break even point (use) of this machine for harvesting own fields and rental fields was 77 h (31 ha) and 266 h (105.5 ha), respectively. Wheat straw chopper is a profitable technology that is getting momentum in combine-harvested wheat fields of the country, which saves chaff for cattle feed and increases the benefit of the farmer. [ABSTRACT FROM AUTHOR]

The article presents a case study of a woman who underwent open surgical mitral valve replacement by using Edwards Sapien-XT valve. The woman presented with dyspnoea and history of chemotherapy and radiotherapy for Hodgkin lymphoma. Cardiac computed tomography was performed which revealed circumferential mitral annular calcification.

Summer itch or insect bite hypersensitivity is common problem during summer in all animals. Some horses are allergic to insect saliva. A 13 year old mare kept for riding purpose at livestock farm FVS, BZU, Multan was examined with a condition characterized by little bumps, papules and abrasions on neck, abdomen, thighs and tail, anaemic mucous membranes, concentrated urine, partial anorexia and generalized emaciation. There was a history of chronic lesion of about more than one year old and many therapies were tried but all non-responsive. A mixed biting infection of Tabanus spp. and Stomoxys spp. was identified. On the basis of clinical sign(s), history and fly identification a diagnosis of insect bite hypersensitivity was detected. Treatment protocol was done in two rounds i.e. first round of treatment was done with dexamethasone along & vitamin E administrated through intramuscular route for 5 days and followed by 10 mg dexamethasone orally for one month. Animal respond the treatment and there was full recovery from condition and on follow up no relapse of the condition was observed. [ABSTRACT FROM AUTHOR]

Analysis of losses in power distribution system and techniques to mitigate these are two active areas of research especially in energy scarce countries like Pakistan to increase the availability of power without installing new generation. Since total energy losses account for both technical losses (TL) as well as non-technical losses (NTLs). Utility companies in developing countries are incurring of major financial losses due to non-technical losses. NTLs lead to a series of additional losses, such as damage to the network (infrastructure and the reduction of network reliability) etc. The purpose of this paper is to perform an introductory investigation of non-technical losses in power distribution systems. Additionally, analysis of NTLs using consumer energy consumption data with the help of Linear Regression Analysis has been carried out. This data focuses on the Low Voltage (LV) distribution network, which includes: residential, commercial, agricultural and industrial consumers by using the monthly kWh interval data acquired over a period (one month) of time using smart meters. In this research different prevention techniques are also discussed to prevent illegal use of electricity in the distribution of electrical power system. [ABSTRACT FROM AUTHOR]

OBJECTIVES: To assess the parental knowledge about Thalassemia. STUDY DESIGN: A descriptive study. PLACE & DURATION: The study was carried out at the Thalassemia Centre of The Children's Hospital & the Institute of Child Health Multan, Pakistan. The duration of the study was 1st January, 2015 to 30th July, 2015. METHODOLOGY: The Data was collected from the 120 registered Thalassemia major patients' parents with the help of convenient sampling. The structured interview schedule as used as a tool for data collection. The structured interview schedule was discussed with two senior doctors (>8 years' experience) working in the Thalassemia Centers and public health Institution. Descriptive and inferential statistics were used to analyze the data which include frequency, percentage. Questions regarding illness, awareness about blood screening, mode of transmission of disease, knowledge and practices abut prevention and treatment of disease was asked. RESULTS: Among the 120 respondents, the majority of the mothers 75.0% were visited to the thalassemia center for the treatment of their thalassemia children. About 73.3% parents were belonging to the Seraiki ethnic background. Sixty three percent parents monthly income was less than 10,000 rupees. Only 50% parents know that thalassemia is an inherited disease. CONCLUSION: The study concluded that the parental knowledge about thalassemia was very poor . Thalassemia awareness program should be proposed for the general public and the Thalassemia prevention program must be introduced at districts levels. OBJECTIVES: To assess the parental knowledge about Thalassemia. STUDY DESIGN: A descriptive study. PLACE & DURATION: The study was carried out at the Thalassemia Centre of The Children's Hospital & the Institute of Child Health Multan, Pakistan. The duration of the study was 1st January, 2015 to 30th July, 2015. METHODOLOGY: The Data was collected from the 120 registered Thalassemia major patients' parents with the help of convenient sampling. The structured interview schedule as used as a tool for data collection. The structured interview schedule was discussed with two senior doctors (>8 years' experience) working in the Thalassemia Centers and public health Institution. Descriptive and inferential statistics were used to analyze the data which include frequency, percentage. Questions regarding illness, awareness about blood screening, mode of transmission of disease, knowledge and practices abut prevention and treatment of disease was asked. RESULTS: Among the 120 respondents, the majority of the mothers 75.0% were visited to the thalassemia center for the treatment of their thalassemia children. About 73.3% parents were belonging to the Seraiki ethnic background. Sixty three percent parents monthly income was less than 10,000 rupees. Only 50% parents know that thalassemia is an inherited disease. CONCLUSION: The study concluded that the parental knowledge about thalassemia was very poor . Thalassemia awareness program should be proposed for the general public and the Thalassemia prevention program must be introduced at districts levels. [ABSTRACT FROM AUTHOR]

In this paper, fast acting dc-link voltage-based energy management schemes are proposed for a hybrid energy storage system fed by solar photovoltaic (PV) energy. Fast dc-link voltage, effective energy management, and reduced current stress on battery are the main features achieved from the proposed control schemes. The main features obtained from the proposed control schemes are tight dc-link voltage regulation, effective power management at the dc-link, allows current limits for both the battery and the supercapacitor. The effectiveness of the proposed control schemes are compared with the unified cascaded control. Using the proposed control schemes, quick fluctuations of load are supplied by the super capacitors and the average load demand is controlled by the batteries. Small-signal control gains are formulated to design the voltage and current loops of the proposed energy management schemes. Detailed stability analysis is also presented to find the boundary values of compensator gains. In addition, a high-gain PV converter is proposed for extraction of maximum power from the solar panels. High voltage gain, reduced reverse recovery of diodes, and less duty cycle operation are the key features obtained from the proposed high-gain converter. The validity of the proposed energy management schemes with high-gain converter is verified by the detailed simulation and experimental studies. [ABSTRACT FROM AUTHOR]

This paper presents design and development of solar power monitoring and control through GSM network in rural application. This system includes a GSM mobile and GSM hardware installed at solar system with 12VDC power for solar power monitoring along with temperatures (ambient and battery). This system is designed to conceptualizing how much solar power transferred to batteries and temperature conditions for that instant of time. Hardware is developed for the continuous update to the targeted station using GSM. The developed hardware gets the signal from the installed location calculate the real time power and temperature parameters. This information transferred to targeted mobile station through GSM interface using texting service (SMS). At the receiving end, power monitoring system is used to maintain the power to batteries profile locally. An easy, cost proficient and consistent working model of whole system has been developed which may be incorporated for data acquisition. Also the same system can use for uninterrupted power supply (UPS) systems. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: TBAJ‐876, developed by TB Alliance, a novel anti‐tuberculosis‐specific drug, has entered Phase II clinical trials. Herein, the first asymmetric synthesis of TBAJ‐876 has been realized using synergistic Li/Li catalysis with excellent yield of 95% and 88 : 12 er (99.6 : 0.4 er, 10 : 1 dr after simple recrystallization). Furthermore, DFT calculations and 7Li‐NMR analysis illustrated the mechanism of the synergistic reaction: a chiral Li‐complex activates the nucleophile to control the stereoselectivity, while the other achiral Li‐complex activates the electrophile to catalyze the carbonyl addition reaction. Additionally, this protocol has been successfully carried out at 5 gram‐scale, showing its industrial potential. [ABSTRACT FROM AUTHOR]

The current trend indicates that energy demand and supply will eventually be controlled by autonomous software that optimizes decision-making and energy distribution operations. New state-of-the-art machine learning (ML) technologies are integral in optimizing decision-making in energy distribution networks and systems. This study was conducted on data-driven probabilistic ML techniques and their real-time applications to smart energy systems and networks to highlight the urgency of this area of research. This study focused on two key areas: i) the use of ML in core energy technologies and ii) the use cases of ML for energy distribution utilities. The core energy technologies include the use of ML in advanced energy materials, energy systems and storage devices, energy efficiency, smart energy material manufacturing in the smart grid paradigm, strategic energy planning, integration of renewable energy, and big data analytics in the smart grid environment. The investigated ML area in energy distribution systems includes energy consumption and price forecasting, the merit order of energy price forecasting, and the consumer lifetime value. Cybersecurity topics for power delivery and utilization, grid edge systems and distributed energy resources, power transmission, and distribution systems are also briefly studied. The primary goal of this work was to identify common issues useful in future studies on ML for smooth energy distribution operations. This study was concluded with many energy perspectives on significant opportunities and challenges. It is noted that if the smart ML automation is used in its targeting energy systems, the utility sector and energy industry could potentially save from $237 billion up to $813 billion. • A study on data-driven probabilistic machine learning (ML) in sustainable smart energy/smart energy systems is conducted. • The use of probabilistic ML in core energy technologies are briefly studied. • The ML techniques play a key role in integrating thermal, electric, large-scale renewable energy resources and fuel gird.A variety of tools for implementing ML in energy systems control, efficient management, and operations are discussed. • Recent key developments of ML, its challenges, and state-of-art future research opportunities are briefly described. [ABSTRACT FROM AUTHOR]

Cigarette smoke (CS)-induced cellular senescence is involved in the pathogenesis of chronic obstructive pulmonary disease (COPD). The molecular mechanism by which CS induces cellular senescence is unknown. Here, we show that CS stress (exposure of primary lung cells to CS extract 0.2-0.75% with a half-maximal inhibitory concentration of ~0.5%) led to impaired mitophagy and perinuclear accumulation of damaged mitochondria associated with cellular senescence in both human lung fibroblasts and small airway epithelial cells (SAECs). Impaired mitophagy was attributed to reduced Parkin translocation to damaged mitochondria, which was due to CS-induced cytoplasmic p53 accumulation and its interaction with Parkin. Impaired Parkin translocation to damaged mitochondria was also observed in mouse lungs with emphysema (6 months CS exposure, 100 mg TPM/m3) as well as in lungs of chronic smokers and patients with COPD. Primary SAECs from patients with COPD also exhibited impaired mitophagy and increased cellular senescence via suborganellar signaling. Mitochondria-targeted antioxidant (Mito-Tempo) restored impaired mitophagy, decreased mitochondrial mass accumulation, and delayed cellular senescence in Parkin-overexpressing cells. In conclusion, defective mitophagy leads to CS stress-induced lung cellular senescence, and restoring mitophagy delays cellular senescence, which provides a promising therapeutic intervention in chronic airway diseases. [ABSTRACT FROM AUTHOR]

We report evidence for the possible application of chitosan-coated nickel-ferrite (NiFe 2 O 4 ) nanoparticles as both T 1 and T 2 contrast agents in magnetic resonance imaging (MRI). The coating of nickel-ferrite nanoparticles with chitosan was performed simultaneously with the synthesis of the nickel-ferrite nanoparticles by a chemical co-precipitation method. The coated nanoparticles were cylindrical in shape with an average length of 17 nm and an average width of 4.4 nm. The bonding of chitosan onto the ferrite nanoparticles was confirmed by Fourier transform infrared spectroscopy. The T 1 and T 2 relaxivities were 0.858±0.04 and 1.71±0.03 mM −1 s −1 , respectively. In animal experimentation, both a 25% signal enhancement in the T 1 -weighted mage and a 71% signal loss in the T 2 -weighted image were observed. This demonstrated that chitosan-coated nickel-ferrite nanoparticles are suitable as both T 1 and T 2 contrast agents in MRI. We note that the applicability of our nanoparticles as both T 1 and T 2 contrast agents is due to their cylindrical shape, which gives rise to both inner and outer sphere processes of nanoparticles. [ABSTRACT FROM AUTHOR]

There is emerging evidence that stem cells can rejuvenate damaged cells by mitochondrial transfer. Earlier studies show that epithelial mitochondrial dysfunction is critical in asthma pathogenesis. Here we show for the first time that Miro1, a mitochondrial Rho- GTPase, regulates intercellular mitochondrial movement from mesenchymal stem cells ( MSC) to epithelial cells ( EC). We demonstrate that overexpression of Miro1 in MSC ( MSCmiroHi) leads to enhanced mitochondrial transfer and rescue of epithelial injury, while Miro1 knockdown ( MSCmiroLo) leads to loss of efficacy. Treatment with MSCmiroHi was associated with greater therapeutic efficacy, when compared to control MSC, in mouse models of rotenone (Rot) induced airway injury and allergic airway inflammation ( AAI). Notably, airway hyperresponsiveness and remodeling were reversed by MSCmiroHi in three separate allergen-induced asthma models. In a human in vitro system, MSCmiroHi reversed mitochondrial dysfunction in bronchial epithelial cells treated with pro-inflammatory supernatant of IL-13-induced macrophages. Anti-inflammatory MSC products like NO, TGF-β, IL-10 and PGE2, were unchanged by Miro1 overexpression, excluding non-specific paracrine effects. In summary, Miro1 overexpression leads to increased stem cell repair. [ABSTRACT FROM AUTHOR]