Poland syndrome with some rare associations, and brief literature review

Poland syndrome (PS) is a rare congenital condition with predominant unilateral chest wall deformity due to hypoplasia of the pectoralis muscles. However, its clinical features are highly variable as all the features may not be present in one individual or it may present with some rare associations or complications as being reported here. A 6-year-old boy was diagnosed, for the first time, as a case of PS but, detailed examination and work up revealed two rare associations and/or incidental findings of this disorder, namely, Dandy-Walker variant and partial anomalous pulmonary venous return. Hence, a detailed clinical examination and a through workup are mandatory to quantify the disease spectrum of this rare disorder

Assessing of Farmers' Opinion towards Floating Agriculture as a Means of Cleaner Production: A Case of Barisal District, Bangladesh

Aims: Bangladesh, as a low-lying country, is vulnerable to global climate change and affected by floods and water logging. Hence, the country needs to adopt sufficient adaptation strategies which are based on local people traditional knowledge and locally available materials; hence, floating agriculture is that type of agriculture. Through this article we examine the floating agriculture related farmers opinion towards floating agriculture as a means of cleaner production Study Design: A descriptive survey research design is followed for the study and interview schedule is the main data collection instrument of the research. Place and Duration of Study: The study area was Banaripara and Wazirpur Upazila of Barisal District of Bangladesh. Data was collected from the farmers who were involved with floating agriculture. Methodology: A total number of 385 farmers of the two Upazilas were the population and out of them 140 farmers were selected as the sample of the study. The interview schedule was developed according to the objective of the research. We used five points Likert scale to judge the opinion towards floating agriculture. We administered multiple regression analysis using SPSS for finding out the influence of farmers’ socio-demographic characteristics on their opinion towards floating agriculture as a means of cleaner production. Results: The farmers of the study area had moderately to less a favorable opinion (83%) towards floating agriculture as a means of cleaner production. Three of the characteristics of the farmers (eg. their age, family size and training participation on floating agriculture) had an influence on their opinion according to the regression results. Therefore, the higher the listed three characteristics according to the regression result the higher will be the opinion of the farmers towards floating agriculture. Conclusion: These findings suggest that it is important to explore knowledge and arrange training for the farmers on floating bed preparation, selecting suitable crops, the intercultural operation of crops and so on. Moreover, future research should be carried out on floating agriculture’s role as a means of women and unemployed employment opportunity, community development, and identify challenges of this technique

Graph Sparsifications using Neural Network Assisted Monte Carlo Tree Search

Graph neural networks have been successful for machine learning, as well as for combinatorial and graph problems such as the Subgraph Isomorphism Problem and the Traveling Salesman Problem. We describe an approach for computing graph sparsifiers by combining a graph neural network and Monte Carlo Tree Search. We first train a graph neural network that takes as input a partial solution and proposes a new node to be added as output. This neural network is then used in a Monte Carlo search to compute a sparsifier. The proposed method consistently outperforms several standard approximation algorithms on different types of graphs and often finds the optimal solution.Comment: arXiv admin note: substantial text overlap with arXiv:2305.0053

Assessing farmers’ awareness towards climate change in the middle part of Bangladesh

Climate change is known to have a severe influence on agriculture, posing a serious danger to the food security and livelihoods of rural people, hence, public understanding about climate change must be encouraged in order to facilitate mitigation and adaptation strategies. Therefore, the main purpose of this study was to determine farmers’ awareness towards climate change. The study was conducted in three villages of Sadar upazila of Gazipur district (middle part) of Bangladesh. A total number of 110 farmers were selected as sample following proportionate random sampling technique. The findings of the study revealed that about 65% farmers were in between 30 to 50 years with secondary to SSC level of education (72%) and had ‘5 to 6’ members of family size. For acquiring extension information, the farmers mainly communicated with SAAOs and seed dealers. Out of all the respondent farmers, 83% had their own land for cultivating different types of crops, and 86% of them received 1 to 6 no. of agriculture related training, although 98% of them did not involve themselves to receive any website help for conducting better agricultural activities. Moreover, 83% farmers had moderately to highly favorable awareness to climate change (both causes and effects). In addition, out of the selected attributes, three attributes, namely, age, agricultural training received, and knowledge on climate change had significant contribution on the awareness to climate change. The study provided new empirical evidence on the awareness level of farmers to climate change on agriculture, in general. The findings of the study can be utilized by the policy makers of the country to formulate proper mitigation and adaptation options to future climate change and its’ impact on agriculture

Solution-processable silicon phthalocyanines in electroluminescent and photovoltaic devices

E.Z.-C. acknowledges the University of St. Andrews for financial support. The authors thank the EPSRC UK National Mass Spectrometry Facility at Swansea University for analytical services. I.D.W.S. acknowledges support from the EPSRC (grant EP/J01771X), the European Research Council (grant 321305), and a Royal Society Wolfson Research Merit Award.Phthalocyanines and their main group and metal complexes are important classes of organic semiconductor materials, but are usually highly insoluble so frequently need to be processed by vacuum deposition in devices. We report two highly soluble silicon phthalocyanine (SiPc) diester compounds and demonstrate their potential as organic semiconductor materials. Near-infrared (λEL = 698-709 nm) solution-processed organic light- emitting diodes (OLEDs) were fabricated and exhibited external quantum efficiencies (EQEs) of up to 1.4%. Binary bulk heterojunction solar cells employing P3HT or PTB7 as the donor and the SiPc as the acceptor provided power conversion efficiencies (PCE) of up to 2.7% under simulated solar illumination. Our results show that soluble SiPcs are promising materials for organic electronics.Publisher PDFPeer reviewe

Staphylococcus aureus Biofilm Infection Compromises Wound Healing by Causing Deficiencies in Granulation Tissue Collagen

Objective: The objective of this work was to causatively link biofilm properties of bacterial infection to specific pathogenic mechanisms in wound healing. Background: Staphylococcus aureus is one of the four most prevalent bacterial species identified in chronic wounds. Causatively linking wound pathology to biofilm properties of bacterial infection is challenging. Thus, isogenic mutant stains of S. aureus with varying degree of biofilm formation ability was studied in an established preclinical porcine model of wound biofilm infection. Methods: Isogenic mutant strains of S. aureus with varying degree (ΔrexB > USA300 > ΔsarA) of biofilm-forming ability were used to infect full-thickness porcine cutaneous wounds. Results: Compared with that of ΔsarA infection, wound biofilm burden was significantly higher in response to ΔrexB or USA300 infection. Biofilm infection caused degradation of cutaneous collagen, specifically collagen 1 (Col1), with ΔrexB being most pathogenic in that regard. Biofilm infection of the wound repressed wound-edge miR-143 causing upregulation of its downstream target gene matrix metalloproteinase-2. Pathogenic rise of collagenolytic matrix metalloproteinase-2 in biofilm-infected wound-edge tissue sharply decreased collagen 1/collagen 3 ratio compromising the biomechanical properties of the repaired skin. Tensile strength of the biofilm infected skin was compromised supporting the notion that healed wounds with a history of biofilm infection are likely to recur. Conclusion: This study provides maiden evidence that chronic S. aureus biofilm infection in wounds results in impaired granulation tissue collagen leading to compromised wound tissue biomechanics. Clinically, such compromise in tissue repair is likely to increase wound recidivism

Integrated Statistical Modeling of Engineering Data with Shared Information

Data has become exceedingly important to organizations, especially with regard to decision making. The large amount of available data is crucial in engineering applications as it ensures the understanding of the problem and the efficient execution of the solution. Studies have also shown that the employment of large amounts of data in engineering applications makes it easier for the data-driven model to generate insights that can be acted on in the best interest of the optimal solution. With the existence of multiple data sources, we can unveil hidden patterns and trends to determine possible relationships in the most complex engineering applications. This has traditionally been achieved by building single statistical models independently to explain single data sources. Nonetheless, when there exists a correlation among several data sources, a single statistical model strategy has been shown to be time consuming, result in loss of pertinent information, and is tedious. It is against this backdrop that this dissertation aimed at developing statistical models that can accurately predict the responses of three important engineering applications. To achieve this aim, this dissertation developed three integrated statistical modeling (ISM) techniques for these three applications. The choosing of the techniques was informed by the fact that they have shown great performance benefits. First was the modeling of multivariate profiles. In some manufacturing processes, profile data are collected to monitor process variations. In situations when multiple profiles are collected together, correlations might exist across profiles. Modeling these multivariate profiles requires describing both within and between profile correlations. Second was the discovery of material oxides, which often suffers from data scarcity. In some cases, collecting data from the target source can be expensive, while there are auxiliary data sources that are cheaper to collect. In such situations, auxiliary data sources can be exploited to improve the performance of the expensive target data. Third was the investigation of data by grouping information where subjects are clustered into various groups. Through employment of the above strategies and applying them to examples and case studies, it was evident that improvement of the prediction accuracy can be realized by exploiting the within-group and between-group characteristics in these data sources, instead of modeling each data source separately. In general, transferring the knowledge across sources is complicated for most real-world systems, and often traditional modeling approaches are not adequate to capture the relations, when data are not stationary or are changing abruptly in a small interval. In addition, the modeling time can be burdensome with the increased number of sources and observations. Hence, developing efficient and flexible frameworks for multiple correlated data sources is imperative. This dissertation proposed novel ISM techniques to deal with the complicated scenarios associated with correlated data sources. This study further demonstrated that the proposed ISM techniques have the ability of helping to model correlation among different data sources into a single modeling framework. The major advantage of the proposed ISM methods was found to be their flexibility over individual modeling of each data sources. The study concludes by proving that it is possible to effectively handle data nonstationarity with reasonable computation loads