Miracle-dz/UniFAR-FTUs huggingface dataset:
Microstructure, joint strength and failure mechanism of Sn-Ag, Sn-Ag-Cu versus Sn-Pb-Ag solders in BGA packages

The microstructure, joint strength and failure mechanisms of Sn-Ag and Sn-Ag-Cu versus Sn-Pb-Ag systems on Cu/Ni/Au BGA pad metallization have been investigated after multiple reflow and high temperature storage. Sn-Pb-Ag system gave rise to a two-layer structure, i.e. Ni/sub 3/Sn/sub 4/ and (Au,Ni)Sn/sub 4/, at the interface after aging at 150/spl deg/C. However, such a structure was not detected in both lead-free systems. Only a layer of Ni/sub 3/Sn/sub 4/ phase in the Sn-Ag system and a layer of Cu-Sn-Ni-Au intermetallic compound in Sn-Ag-Cu system were found at the interfaces, even after 1000 hours at the afore-mentioned temperature. The formation of the (Au,Ni)Sn/sub 4/ ternary compound was due to re-settlement of Au at the interface which led to severe brittle failure in the Sn-Pb-Ag system. In contrast, Sn-Ag and Sn-Ag-Cu systems failed exclusively inside the solder after aging at 150/spl deg/C up to 1000 hours. The solder ball joint strength of the three systems and failure modes were also evaluated. Both lead-free systems showed good resistance to thermal aging with a solder ball joint strength maintained at about 1.60 to 1.70 kgf. The Sn-Pb-Ag system, on the other hand, degraded in mechanical performance over aging time, reaching a strength as low as 1.20 kgf. The growth rates of intermetallic layers at 125, 150, and 175/spl deg/C, and the activation energy were also determined in this study.
Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection

Physically-based approaches for estimating Leaf Area Index (LAI) using remote sensing data rely on radiative transfer (RT) models. Currently, many RT models are freely available, but determining the appropriate RT model for LAI retrieval is still problematic. This study aims to evaluate the necessity of RT model selection for LAI retrieval and to propose a retrieval methodology using different RT models for different vegetation types. Both actual experimental observations and RT model simulations were used to conduct the evaluation. Each of them includes needleleaf forests and croplands, which have contrasting structural attributes. The scattering from arbitrarily inclined leaves (SAIL) model and the four-scale model, which are 1D and 3D RT models, respectively, were used to simulate the synthetic test datasets. The experimental test dataset was established through two field campaigns conducted in the Heihe River Basin. The results show that the realistic representation of canopy structure in RT models is very important for LAI retrieval. If an unsuitable RT model is used, then the root mean squared error (RMSE) will increase from 0.43 to 0.60 in croplands and from 0.52 to 0.63 in forests. In addition, an RT model’s potential to retrieve LAI is limited by the availability of a priori information on RT model parameters. 3D RT models require more a priori information, which makes them have poorer generalization capability than 1D models. Therefore, physically-based retrieval algorithms should embed more than one RT model to account for the availability of a priori information and variations in structural attributes among different vegetation types.
Building the right centriole for each cell type

The centriole is a multifunctional structure that organizes centrosomes and cilia and is important for cell signaling, cell cycle progression, polarity, and motility. Defects in centriole number and structure are associated with human diseases including cancer and ciliopathies. Discovery of the centriole dates back to the 19th century. However, recent advances in genetic and biochemical tools, development of high-resolution microscopy, and identification of centriole components have accelerated our understanding of its assembly, function, evolution, and its role in human disease. The centriole is an evolutionarily conserved structure built from highly conserved proteins and is present in all branches of the eukaryotic tree of life. However, centriole number, size, and organization varies among different organisms and even cell types within a single organism, reflecting its cell type–specialized functions. In this review, we provide an overview of our current understanding of centriole biogenesis and how variations around the same theme generate alternatives for centriole formation and function.
Increased postural sway during quiet stance as a risk factor for prospective falls in community-dwelling elderly individuals

Objective fall-related injuries constitute major health risks in older individuals, and these risks are projected to increase in parallel with increasing human longevity. Impaired postural stability is a potential risk factor related to falls, although the evidence is inconclusive, partly due to the lack of prospective studies. This study aimed to investigate how objective measures of postural sway predict incident falls. Design, setting and participants this prospectively observational study included 1,877 community-dwelling individuals aged 70 years who participated in the Healthy Ageing Initiative between June 2012 and December 2015. Measurements postural sway was measured during eyes-open (EO) and eyes-closed (EC) trials using the Wii Balance Board. Functional mobility, muscle strength, objective physical activity and cognitive performance were also measured. Participants reported incident falls 6 and 12 months after the examination. Results during follow-up, 255 (14%) prospective fallers were identified. Division of centre of pressure (COP) sway lengths into quintiles revealed a nonlinear distribution of falls for EO trial data, but not EC trial data. After adjustment for multiple confounders, fall risk was increased by 75% for participants with COP sway lengths ≥400 mm during the EO trial (odds ratio [OR] 1.75, 95% confidence interval [CI] 1.09-2.79), and approximately doubled for sway lengths ≥920 mm during the EC trial (OR 1.90, 95% CI 1.12-3.22). Conclusion objective measures of postural sway independently predict incident falls in older community-dwelling men and women. Further studies are needed to evaluate whether postural sway length is of interest for the prediction of incident falls in clinical settings.
Genetic Transformation for Metabolic Engineering of Tropane Alkaloids

1.1 Tropane alkaloids: history and relevance Historically, plants have been the major source of active compounds for the pharmaceutical industry. Today, plant extracts and its active principles represent 25% of the annual commercialized drugs in the United States (Kinghorn and Seo, 1996, Buttler 2004, Prakash Rout et al., 2009, Qurishi et al., 2010). Moreover, the World Health Organization has estimated that more than the 80% of the population in developing countries relies for their health care on traditional medicines mostly from plant origin (Canter et al., 2005). The plant active principles are, in general, a product of the plant secondary metabolism; they are lowmolecular-weight compounds that in general participate in defence mechanisms against diseases (phytoalexins) or as attractants for pollinator insects (pigments and fragrances) (Canter et al., 2005). The production of each group of secondary metabolites, as counterpart of primary metabolites, is in general restricted to a limited number of species, and they usually are organ-specific and furthermore, tissue-specific. According to their chemical properties, secondary metabolites could be classified in three main groups: a) terpenes, b) phenols and c) alkaloids. Terpenes and terpenoids are the primary constituents of the essential oils, widely used as natural flavour additives for food and fragrances. Phenols are a class of chemical compounds consisting of a hydroxyl group directly bonded to an aromatic hydrocarbon group that seem to be universally distributed in plants. They are essential for the growth and reproduction of plants, and are produced as a response for defending injured plants against pathogens. Finally, alkaloids are characterized for being alkaline nitrogenated organic compounds, derived from aminoacids, generally insoluble in water and soluble in alcohol, ether, chloroform, etc. Among the alkaloids, those derived from tropane have received particular attention for their properties.
Impact of metformin on C-reactive protein levels in women with polycystic ovary syndrome: a meta-analysis

The impact of the recommended first-line treatment with metformin on C-reactive protein (CRP) levels in patients with polycystic ovary syndrome (PCOS) is still controversial. We conducted a meta-analysis of studies reporting the impact of metformin on serum CRP levels in women with PCOS. The weighted mean differences (WMDs) and the corresponding 95% confidence intervals (CIs) were used to assesse the effects. GRADE approach was used to assesse the quality of the evidence. A total of 20 studies that included 433 women with PCOS were analyzed. CRP levels significantly decreased after metformin treatment (WMD = -1.23mg/L, 95%CI: -1.65 to -0.81, I2 = 93% and P < 0.001 for heterogeneity). The decreased levels of CRP were observed both in lean (BMI<25 kg/m2) and obese (BMI>25 kg/m2) patients. Interestingly, the degree of decreased CRP levels was not depended on metformin dosage, but more significantly in patients treated beyond 6 months (WMD≥6months = -1.47mg/L vs. WMD<6months = -0.94 mg/L). Decreased CRP levels are not associated with the status of IR and androgen in patients with PCOS. However, the quality of evidence was very low because of the limitations and inconsistency of the included studies. Therefore, metformin shows the potential effects on CRP levels in women with PCOS. However, considering the very low quality of evidence for the analysis, the effect of metformin on CRP levels are still very uncertain, and large-scale randomized-controlled study is needed to ascertain the long-term effects of metformin in PCOS.
‘Of course we are on Facebook’: Use and non-use of social media among Swedish and Norwegian politicians

While plenty of research has provided important insights into the uses of the Internet by politicians during elections, a relatively scarce amount of work has looked into these uses outside of such parliamentary events. This article seeks to remedy this lack of research by presenting a study on the ‘routine’ uses of two of the currently most popular social media services – Facebook and Twitter. Focusing on politicians elected to the national parliaments of Norway and Sweden, the article employs novel methodologies for data collection and statistical analyses in order to provide an overarching, structural view of the day-to-day social media practices of Scandinavian politicians. Findings indicate that use levels are rather low for both services – the median amount of tweets sent and messages posted on Facebook is close to one per day. Further analyses reveal that the most active politicians could be labelled as ‘underdogs’, as they are more likely to be younger, in opposition and out of the political limelight.
Menstrual Cycle Hormone Changes in Women Traversing Menopause: Study of Women’s Health Across the Nation

Context Menstrual cycle hormone patterns in women approaching menopause are inadequately studied. Objective To describe day-to-day menstrual cycle hormones in women as they approach menopause from the Study of Women's Health Across the Nation Daily Hormone Study (DHS). Design DHS enrollees collected daily urine for one entire menstrual cycle or up to 50 days, whichever came first, annually, up to the final menstrual period (FMP) or for up to 10 years. Setting Seven sites across the United States. Participants A total of 511 premenopausal or early perimenopausal women at enrollment, within 10 years before menopause. Intervention Time-to-FMP measurement. Main Outcome Measures Evidence of luteal activity (ELA), determined using objective algorithms. Menstrual cycle/segment length; whole cycle, and segment integrated urinary luteinizing hormone, follicle-stimulating hormone, estrone conjugates, and pregnanediol glucuronide (Pdg) for each year, organized around the FMP. Results Mean menstrual cycle length was remarkably preserved at 26 to 27 days in ELA cycles; non-ELA cycles had greater variability. The percentage of cycles that were ELA remained high until 5 years before the FMP (87.9%); only 22.8% of cycles within 1 year of the FMP were ELA. Whole cycle hormones remained relatively stable up to 3 years before the FMP, when gonadotropins began to increase. Pdg excretion declined slowly with progress to the FMP, but Pdg patterns of ELA cycles remained distinguishable from non-ELA. Conclusions Menstrual cycle hormone patterns in perimenopausal women resemble those of midreproductive-aged women until 5 years before menopause, and presumably ovulatory cycles retain a potentially fertile pattern up to the end of reproductive life.
ANTIMICROBIAL PROPERTY OF DIFFERENT PARTS OF CITRUS LIMON FROM N.E. INDIA

Lemon (Citrus) are acidic fruits of family Rutaceae. Lemon is called the tree of life because of its various medicinal properties. Almost all parts of the fruit can be used against different diseases due to its wide range of medicinal property. Other than nutritional content they also have antioxidant, antimicrobial, insect repellent, medicinal and many other properties. Different biocompoundsof the fruit can replace the function of synthetic drugs which are available in market. In the present study, the antimicrobial extent of crude juice, different extracts of leaf (matured and young), peel and standards streptomycin and tetracycline (1mg/mL) was determined against E. coli by agar diffusion method. The highest and minimum inhibition zone was found in crude juice (more than the standard) and matured leaf respectively. MIC for the crude juice was recorded as 0. 01μL/mL (v/v) and for streptomycin and tetracycline was recorded as 0. 1μg/mL and 0. 01μg/mL respectively. The study clearly points out the antimicrobial potential emphasizing the importance of incorporating lemon as a regular component in diet.
Dynamic modeling of DC–DC converters with peak current control in double‐stage photovoltaic grid‐connected inverters

In photovoltaic (PV) double‐stage grid‐connected inverters a high‐frequency DC–DC isolation and voltage step‐up stage is commonly used between the panel and the grid‐connected inverter. This paper is focused on the modeling and control design of DC–DC converters with Peak Current mode Control (PCC) and an external control loop of the PV panel voltage, which works following a voltage reference provided by a maximum power point tracking (MPPT) algorithm. In the proposed overall control structure the output voltage of the DC–DC converter is regulated by the grid‐connected inverter. Therefore, the inverter may be considered as a constant voltage load for the development of the small‐signal model of the DC–DC converter, whereas the PV panel is considered as a negative resistance. The sensitivity of the control loops to variations of the power extracted from the PV panel and of its voltage is studied.
Commercially Available Smartphone Apps to Support Postoperative Pain Self-Management: Scoping Review

Background Recently, the use of smartphones to deliver health-related content has experienced rapid growth, with more than 165,000 mobile health (mHealth) apps currently available in the digital marketplace. With 3 out of 4 Canadians currently owning a smartphone, mHealth apps offer opportunities to deliver accessible health-related knowledge and support. Many individuals experience pain after surgery, which can negatively impact their health-related quality of life, including sleep, emotional, and social functioning. Smartphone apps that provide remote real-time monitoring and symptom management have the potential to improve self-management skills in patients experiencing postoperative pain. Increased confidence and practice of self-management skills could contribute to decreased postoperative pain and reduce risk of developing persistent pain. Published reviews of general pain self-management apps demonstrate a lack of evidence-based content, theoretical grounding, and health care professional involvement. However, no review to date has focused on the app marketplace specific for individuals with postoperative pain. Objective The aim of this study was to characterize and critically appraise the content and functionality of commercially available postoperative pain self-management apps. Methods An electronic search and extraction was conducted between December 2016 and March 2017 of the official Canadian app stores for the three major smartphone operating systems (iPhone operating system [iOS], Android, and Windows). Stores were searched separately using predetermined search terms. Two authors screened apps based on information provided in the public app description. Metadata from all included apps were abstracted into a standard spreadsheet. Two authors verified the data with reference to the apps and downloaded apps themselves. The content and functionality of each app as it pertained to postoperative pain self-management was rated. Results A total of 10 apps met the inclusion criteria. All included apps were designed exclusively for the Android platform. Education was the most common self-management feature offered (8/10, 80%), with none of the apps offering features related to goal setting or social support. Overall, no single app was comprehensive in terms of pain self-management content. Five (50%) apps reported the involvement of a health care provider in their development. However, not a single app involved end users in their development, and none of the apps underwent scientific evaluation. Additionally, none of the apps were designed for use in pediatric patients. Conclusions Currently available postoperative pain apps for patients lack evidence-based content, goal setting, and social support functions. There is a need to develop and test comprehensive theory-based apps to support patients with pain self-management care following surgery.
Explaining Black-box Android Malware Detection

Machine-learning models have been recently used for detecting malicious Android applications, reporting impressive performances on benchmark datasets, even when trained only on features statically extracted from the application, such as system calls and permissions. However, recent findings have highlighted the fragility of such in-vitro evaluations with benchmark datasets, showing that very few changes to the content of Android malware may suffice to evade detection. How can we thus trust that a malware detector performing well on benchmark data will continue to do so when deployed in an operating environment? To mitigate this issue, the most popular Android malware detectors use linear, explainable machine-learning models to easily identify the most influential features contributing to each decision. In this work, we generalize this approach to any black-box machine-learning model, by leveraging a gradient-based approach to identify the most influential local features. This enables using nonlinear models to potentially increase accuracy without sacrificing interpretability of decisions. Our approach also highlights the global characteristics learned by the model to discriminate between benign and malware applications. Finally, as shown by our empirical analysis on a popular Android malware detection task, it also helps identifying potential vulnerabilities of linear and nonlinear models against adversarial manipulations.
Barriers to living donor kidney transplantation in the United Kingdom: a national observational study

ABSTRACT Background. Living donor kidney transplantation (LDKT) provides more timely access to transplantation and better clinical outcomes than deceased donor kidney transplantation (DDKT). This study investigated disparities in the utilization of LDKT in the UK. Methods. A total of 2055 adults undergoing kidney transplantation between November 2011 and March 2013 were prospectively recruited from all 23 UK transplant centres as part of the Access to Transplantation and Transplant Outcome Measures (ATTOM) study. Recipient variables independently associated with receipt of LDKT versus DDKT were identified. Results. Of the 2055 patients, 807 (39.3%) received LDKT and 1248 (60.7%) received DDKT. Multivariable modelling demonstrated a significant reduction in the likelihood of LDKT for older age {odds ratio [OR] 0.11 [95% confidence interval (CI) 0.08–0.17], P < 0.0001 for 65–75 years versus 18–34 years}; Asian ethnicity [OR 0.55 (95% CI 0.39–0.77), P = 0.0006 versus White]; Black ethnicity [OR 0.64 (95% CI 0.42–0.99), P = 0.047 versus White]; divorced, separated or widowed [OR 0.63 (95% CI 0.46–0.88), P = 0.030 versus married]; no qualifications [OR 0.55 (95% CI 0.42–0.74), P < 0.0001 versus higher education qualifications]; no car ownership [OR 0.51 (95% CI 0.37–0.72), P = 0.0001] and no home ownership [OR 0.65 (95% CI 0.85–0.79), P = 0.002]. The odds of LDKT varied significantly between countries in the UK. Conclusions. Among patients undergoing kidney transplantation in the UK, there are significant age, ethnic, socio-economic and geographic disparities in the utilization of LDKT. Further work is needed to explore the potential for targeted interventions to improve equity in living donor transplantation.
Towards an MDD Based Framework for Self Adaptive IoT Applications Development

As technology and communication advances, more devices (and things) are able to connect to the Internet and talk to each other to achieve a common goal which results in the emergence of the Internet of Things (IoT) era. It is believed that IoT will bring up a limitless number of applications and business opportunities that will affect almost every aspect of our life. Research has already been conducted to investigate the challenges that obstruct the realization of IoT along with the promising solutions that pave the way for the acceptance and enabling of IoT. Among the research areas that is of a great importance to making IoT paradigm possible is the presence of a unified programming framework that masks the heterogeneity of the involved devices of the IoT platform. Such a framework guides system developers throughout the IoT application development process. In this paper, we investigate the IoT concept and its high level architecture in general and focus more on the application development aspect. We believe that IoT applications are highly dynamic in nature and thus need to be engineered with the self adaptive and autonomic concepts in mind. Therefore, our proposed IoT software development lifecycle was based on the IBM architecture blueprint for autonomic systems. To cater for the runtime dynamic and heterogeneity aspects of IoT applications, we adopt the MDD paradigm for our proposed development framework. We highlight the core requirements of a resilient development framework that accommodates the necessary concepts and processes for a successful IoT application.