Influence of Tillage and Poultry Manure on the Physical Properties of Grain and Yield Attributes of Spring Maize (Zea mays L.)

Grains are the economical part of maize that demand proper management practices to achieve the crop potential. The study explored the influence of different tillage practices and poultry manure levels on the grain length, breadth, area, grains weight per cob and grains yield per m2of maize at Agronomic Research Area, University of Agriculture, Faisalabad, Pakistan, during spring 2010 and 2011. The experiment was laid out in randomized complete block design with split plot arrangement, having four tillage practices as main plot treatments, zero tillage (direct seed sowing with dibbler), minimum tillage (one cultivation with normal cultivator followed by planking), conventional tillage (2–3 cultivations with normal cultivator followed by planking) and deep tillage (two deep ploughing with chisel plough + one cultivation with normal cultivator followed by planking). Sub plot treatments were three poultry manure levels; control (no poultry manure), poultry manure at the amount of 5 Mg ha-1 and poultry manure at 10 Mg ha-1. Data indicated that the deep tillage practice significantly improved the maize grain physical properties and yield over the other tillage practices in both years of study. Increasing order of poultry manure dose treatments produced the good and healthy seeds over the control treatment. A positive correlation between grain yield, physical properties of maize grain and grains weight per cob was recorded

Inter-rater reliability of physiotherapists using the Action Research Arm Test in chronic stroke

The purpose of this study is to establish whether physiotherapists' ratings are consistent, when using the Action Research Arm Test (ARAT) to score a chronic stroke patient. This was part of a large project establishing the reliability in chronic stroke. This study used a correlational design comparing the association between physiotherapist scores of the same patient, to establish the ARAT's inter-rater reliability. The COSMIN checklist was followed to enhance the methodology of the study. Twenty physiotherapists (8 female and 12 male) aged between 25 and 53 years were selected. There were no participant dropouts or withdrawals. The sample size was normally distributed. The physiotherapists appeared representative of the UK physiotherapy population, with the exception of gender. The distribution of scores showed a normal distribution with standard deviation of score of 1.9. The Kendall's W test showed 0.711 of agreement between the raters. The scores achieved statistical significance showing consistency between physiotherapists' scores with chronic stroke. Limitations of the study were the use of a small single center convenience sample that may reduce the generalizability of the findings. The ARAT is consistent when scored by physiotherapists in a chronic stroke population. The inter-rater reliability range was (0.70 to 0.90) which is categorized as good

Multi-organ point-of-care ultrasound for COVID-19 (PoCUS4COVID): international expert consensus.

COVID-19 has caused great devastation in the past year. Multi-organ point-of-care ultrasound (PoCUS) including lung ultrasound (LUS) and focused cardiac ultrasound (FoCUS) as a clinical adjunct has played a significant role in triaging, diagnosis and medical management of COVID-19 patients. The expert panel from 27 countries and 6 continents with considerable experience of direct application of PoCUS on COVID-19 patients presents evidence-based consensus using GRADE methodology for the quality of evidence and an expedited, modified-Delphi process for the strength of expert consensus. The use of ultrasound is suggested in many clinical situations related to respiratory, cardiovascular and thromboembolic aspects of COVID-19, comparing well with other imaging modalities. The limitations due to insufficient data are highlighted as opportunities for future research.post-print2.282 K

Analyzing D-wave quantum macro assembler security

As we enter the quantum computing era, security becomes of at most importance. With the release of D-Wave One in 2011 and most recently the 2000Q, with 2,000 qubits, and with NASA and Google using D-wave Systems quantum computers, a thorough examination of quantum computer security is needed. Quantum computers underlying hardware is not compatible with classical boolean and binary-based computer systems and software. Assemblers and compliers translate modern programming languages and problems into quantum-annealing methods compatible with quantum computers. This paper presents a vulnerability assessment utilizing static source code analysis on Qmasm Python tool. More specifically, we use flow-sensitive, inter-procedural and context-sensitive data flow analysis to uncover vulnerable points in the program. We demonstrate the Qmasm security flaws that can leave D-Wave 2X system vulnerable to severe threats

Analyzing D-wave quantum macro assembler security

As we enter the quantum computing era, security becomes of at most importance. With the release of D-Wave One in 2011 and most recently the 2000Q, with 2,000 qubits, and with NASA and Google using D-wave Systems quantum computers, a thorough examination of quantum computer security is needed. Quantum computers underlying hardware is not compatible with classical boolean and binary-based computer systems and software. Assemblers and compliers translate modern programming languages and problems into quantum-annealing methods compatible with quantum computers. This paper presents a vulnerability assessment utilizing static source code analysis on Qmasm Python tool. More specifically, we use flow-sensitive, inter-procedural and context-sensitive data flow analysis to uncover vulnerable points in the program. We demonstrate the Qmasm security flaws that can leave D-Wave 2X system vulnerable to severe threats

Nature inspired meta-heuristic algorithms for deep learning: recent progress and novel perspective

Deep learning is presently attracting extra ordinary attention from both the industry and the academia. The application of deep learning in computer vision has recently gain popularity. The optimization of deep learning models through nature inspired algorithms is a subject of debate in computer science. The application areas of the hybrid of natured inspired algorithms and deep learning architecture includes: machine vision and learning, image processing, data science, autonomous vehicles, medical image analysis, biometrics, etc. In this paper, we present recent progress on the application of nature inspired algorithms in deep learning. The survey pointed out recent development issues, strengths, weaknesses and prospects for future research. A new taxonomy is created based on natured inspired algorithms for deep learning. The trend of the publications in this domain is depicted; it shows the research area is growing but slowly. The deep learning architectures not exploit by the nature inspired algorithms for optimization are unveiled. We believed that the survey can facilitate synergy between the nature inspired algorithms and deep learning research communities. As such, massive attention can be expected in a near future

Quantitative Whole Body Biodistribution of Fluorescent-Labeled Agents by Non-Invasive Tomographic Imaging

When small molecules or proteins are injected into live animals, their physical and chemical properties will significantly affect pharmacokinetics, tissue penetration, and the ultimate routes of metabolism and clearance. Fluorescence molecular tomography (FMT) offers the ability to non-invasively image and quantify temporal changes in fluorescence throughout the major organ systems of living animals, in a manner analogous to traditional approaches with radiolabeled agents. This approach is best used with biotherapeutics (therapeutic antibodies, or other large proteins) or large-scaffold drug-delivery vectors, that are minimally affected by low-level fluorophore conjugation. Application to small molecule drugs should take into account the significant impact of fluorophore labeling on size and physicochemical properties, however, the presents studies show that this technique is readily applied to small molecule agents developed for far-red (FR) or near infrared (NIR) imaging. Quantification by non-invasive FMT correlated well with both fluorescence from tissue homogenates as well as with planar (2D) fluorescence reflectance imaging of excised intact organs (r2 = 0.996 and 0.969, respectively). Dynamic FMT imaging (multiple times from 0 to 24 h) performed in live mice after the injection of four different FR/NIR-labeled agents, including immunoglobulin, 20–50 nm nanoparticles, a large vascular imaging agent, and a small molecule integrin antagonist, showed clear differences in the percentage of injected dose per gram of tissue (%ID/g) in liver, kidney, and bladder signal. Nanoparticles and IgG1 favored liver over kidney signal, the small molecule integrin-binding agent favored rapid kidney and bladder clearance, and the vascular agent, showed both liver and kidney clearance. Further assessment of the volume of distribution of these agents by fluorescent volume added information regarding their biodistribution and highlighted the relatively poor extravasation into tissue by IgG1. These studies demonstrate the ability of quantitative FMT imaging of FR/NIR agents to non-invasively visualize and quantify the biodistribution of different agents over time