A linear model for leaf area measurement to screen potential leaf material for herbal drug in Adhatoda vasica L.

Leaf area is an important parameter in physiology and agronomy studies. Linear models for leaf area measurement are developed for plant species as a nondestructive method. The plant Adhatoda vasica L. (a medicinal plant) was selected and the leaves of this plant were used for development of linear model for leaf area using Leaf Area Meter (LAM) software. Planimetric parameters (length, length2, width and width2) and gravimetric (dry weight and water content) parameters are considered for the development of linear model for this plant species. Single factor ANOVA and linear correlations were worked out using these parameters and leaf area. The plant was showed significant relationship with the parameters studied. The best correlation as represented by regression coefficient (R2) was used and improved R2 is worked out. It is observed that with increase in leaf area, water content is also increased and showed best correlation with the leaf area. Thus water content can be taken as a parameter for developing linear model for leaf area is concluded

Imaging biomarkers of adiposity and sarcopenia as potential predictors for overall survival among patients with endometrial cancer treated with bevacizumab

Objective:To examine associations of body mass index (BMI), subcutaneous fat area (SFA) and density (SFD), visceral fat area (VFA) and density (VFD) and total psoas area (TPA) to outcomes among patients receiving chemotherapy with or without bevacizumab for advanced or recurrent endometrial cancer (EC). Methods:This was a multi-institutional, retrospective study of patients with EC treated with and without bevacizumab as part of front-line, platinum based chemotherapy. Demographics and clinical characteristics were collected. SFA, VFA, SFD, VFD, and TPA were determined from pre-treatment CT scans using a deep learning algorithm. Data was compared with overall survival (OS) and progression free survival (PFS). Results:Seventy-eight patients were analyzed. The majority were Caucasian (87.2%) with a mean BMI of 34.7 kg/m2. PFS and OS did not differ between patients with BMI, SFA, VFA, SFD, VFD, or TPA ≥ the 50th percentile compared to <50th percentile (p = 0.91, 0.45, 0.71, 0.74, 0.60, and 0.74 respectively) and (p = 0.99, 0.59, 0.14, 0.77, and 0.85 respectively). When adjusting for prognostic factors, elevated VFA trended towards shorter OS (25.1 vs 59.5 months, HR = 1.68 [0.92-3.05]).Patients receiving bevacizumab had similar OS compared to those who did not (37.6 vs 44.5 months, p = 0.409). When stratified by adiposity markers, no subset demonstrated benefit from bevacizumab. Conclusion:Obesity has been associated with increased levels of vascular endothelial growth factor (VEGF), the main target for bevacizumab therapy. Imaging measurements of VFA may provide prognostic information for patients with EC but no adiposity marker was predictive of improved response to bevacizumab

Comparison of two-phase pipe flow in openFOAM with a mechanistic model

Two-phase pipe flow is a common occurrence in many industrial applications such as power generation and oil and gas transportation. Accurate prediction of liquid holdup and pressure drop is of vast importance to ensure effective design and operation of fluid transport systems. In this paper, a Computational Fluid Dynamics (CFD) study of a two-phase flow of air and water is performed using OpenFOAM. The two-phase solver, interFoam is used to identify flow patterns and generate values of liquid holdup and pressure drop, which are compared to results obtained from a two-phase mechanistic model developed by Petalas and Aziz (2002). A total of 60 simulations have been performed at three separate pipe inclinations of 0°, +10° and -10° respectively. A three dimensional, 0.052m diameter pipe of 4m length is used with the Shear Stress Transport (SST) k - turbulence model to solve the turbulent mixtures of air and water. Results show that the flow pattern behaviour and numerical values of liquid holdup and pressure drop compare reasonably well to the mechanistic model

Large Scale Modular Quantum Computer Architecture with Atomic Memory and Photonic Interconnects

The practical construction of scalable quantum computer hardware capable of executing non-trivial quantum algorithms will require the juxtaposition of different types of quantum systems. We analyze a modular ion trap quantum computer architecture with a hierarchy of interactions that can scale to very large numbers of qubits. Local entangling quantum gates between qubit memories within a single register are accomplished using natural interactions between the qubits, and entanglement between separate registers is completed via a probabilistic photonic interface between qubits in different registers, even over large distances. We show that this architecture can be made fault-tolerant, and demonstrate its viability for fault-tolerant execution of modest size quantum circuits

Radiation therapy for vaginal and perirectal lesions in recurrent ovarian cancer

The role for localized radiation to treat ovarian cancer (OC) patients with locally recurrent vaginal/perirectal lesions remains unclear, though we hypothesize these patients may be salvaged locally and gain long-term survival benefit. We describe our institutional outcomes using intensity modulated radiation therapy (IMRT) +/- high-dose rate (HDR) brachytherapy to treat this population. Our primary objectives were to evaluate complete response rates of targeted lesions after radiation and calculate our 5-year in-field control (IFC) rate. Secondary objectives were to assess radiation-related toxicities, chemotherapy free-interval (CFI), as well as post-radiation progression-free (PFS) and overall survival (OS). PFS and OS were defined from radiation start to either progression or death/last follow-up, respectively. This was a heavily pre-treated cohort of 17 recurrent OC patients with a median follow-up of 28.4 months (range 4.5-166.4) after radiation completion. 52.9% had high-grade serous histology and 4 (23.5%) had isolated vaginal/perirectal disease. Four (23.5%) patients had in-field failures at 3.7, 11.2, 24.5, and 27.5 months after start of radiation, all treated with definitive dosing of radiation therapy. Patients who were platinum-sensitive prior to radiation had similar median PFS (6.5 vs. 13.4 months, log-rank p = 0.75), but longer OS (71.1 vs 18.8 months, log-rank p = 0.05) than their platinum-resistant counterparts. Excluding patients with low-grade histology or who were treated with palliative radiation, median CFI was 14.2 months (range 4.7 - 33.0). Radiation was well tolerated with 2 (12.0%) experiencing grade 3/4 gastrointestinal/genitourinary toxicities. In conclusion, radiation to treat locally recurrent vaginal/perirectal lesions in heavily pre-treated OC patients is safe and may effectively provide IFC

Evaluating end-to-end optimization for data analytics applications in weld

Modern analytics applications use a diverse mix of libraries and functions. Unfortunately, there is no optimization across these libraries, resulting in performance penalties as high as an order of magnitude in many applications. To address this problem, we proposed Weld, a common runtime for existing data analytics libraries that performs key physical optimizations such as pipelining under existing, imperative library APIs. In this work, we further develop the Weld vision by designing an automatic adaptive optimizer for Weld applications, and evaluating its impact on realistic data science workloads. Our optimizer eliminates multiple forms of overhead that arise when composing imperative libraries like Pandas and NumPy, and uses lightweight measurements to make data-dependent decisions at run-time in ad-hoc workloads where no statistics are available, with sub-second overhead. We also evaluate which optimizations have the largest impact in practice and whether Weld can be integrated into libraries incrementally. Our results are promising: using our optimizer, Weld accelerates data science workloads by up to 23X on one thread and 80X on eight threads, and its adaptive optimizations provide up to a 3.75X speedup over rule-based optimization. Moreover, Weld provides benefits if even just 4--5 operators in a library are ported to use it. Our results show that common runtime designs like Weld may be a viable approach to accelerate analytics

Why a clearer ‘green industrial policy’ matters for India: Reconciling growth, climate change and inequality

© 2016, © The Author(s) 2016. To ensure a healthy growth of the economy particularly in the manufacturing sector, the Indian Government is more than ever focussed on promoting the use of sustainable and affordable energy resources. Recent initiatives such as the Solar Cities Development Programme are a good example. However, in order for these initiatives to gain legitimacy as part of a new ‘green industrial policy’, the Indian Government needs to do more, especially by bringing on board strategies for combating poverty within the gamut of this emerging ‘green industrial policy’ as well as to re-think India’s position on global conventions on climate change

Learning perceptually grounded word meanings from unaligned parallel data

In order for robots to effectively understand natural language commands, they must be able to acquire meaning representations that can be mapped to perceptual features in the external world. Previous approaches to learning these grounded meaning representations require detailed annotations at training time. In this paper, we present an approach to grounded language acquisition which is capable of jointly learning a policy for following natural language commands such as “Pick up the tire pallet,” as well as a mapping between specific phrases in the language and aspects of the external world; for example the mapping between the words “the tire pallet” and a specific object in the environment. Our approach assumes a parametric form for the policy that the robot uses to choose actions in response to a natural language command that factors based on the structure of the language. We use a gradient method to optimize model parameters. Our evaluation demonstrates the effectiveness of the model on a corpus of commands given to a robotic forklift by untrained users.U.S. Army Research Laboratory (Collaborative Technology Alliance Program, Cooperative Agreement W911NF-10-2-0016)United States. Office of Naval Research (MURIs N00014-07-1-0749)United States. Army Research Office (MURI N00014-11-1-0688)United States. Defense Advanced Research Projects Agency (DARPA BOLT program under contract HR0011-11-2-0008

Altered Velocity Processing in Schizophrenia during Pursuit Eye Tracking

Smooth pursuit eye movements (SPEM) are needed to keep the retinal image of slowly moving objects within the fovea. Depending on the task, about 50%–80% of patients with schizophrenia have difficulties in maintaining SPEM. We designed a study that comprised different target velocities as well as testing for internal (extraretinal) guidance of SPEM in the absence of a visual target. We applied event-related fMRI by presenting four velocities (5, 10, 15, 20°/s) both with and without intervals of target blanking. 17 patients and 16 healthy participants were included. Eye movements were registered during scanning sessions. Statistical analysis included mixed ANOVAs and regression analyses of the target velocity on the Blood Oxygen Level Dependency (BOLD) signal. The main effect group and the interaction of velocity×group revealed reduced activation in V5 and putamen but increased activation of cerebellar regions in patients. Regression analysis showed that activation in supplementary eye field, putamen, and cerebellum was not correlated to target velocity in patients in contrast to controls. Furthermore, activation in V5 and in intraparietal sulcus (putative LIP) bilaterally was less strongly correlated to target velocity in patients than controls. Altered correlation of target velocity and neural activation in the cortical network supporting SPEM (V5, SEF, LIP, putamen) implies impaired transformation of the visual motion signal into an adequate motor command in patients. Cerebellar regions seem to be involved in compensatory mechanisms although cerebellar activity in patients was not related to target velocity

Developmental features of cotton fibre middle lamellae in relation to cell adhesion and cell detachment in cultivars with distinct fibre qualities.

Background: Cotton fibre quality traits such as fibre length, strength, and degree of maturation are determined by genotype and environment during the sequential phases of cotton fibre development (cell elongation, transition to secondary cell wall construction and cellulose deposition). The cotton fibre middle lamella (CFML) is crucial for both cell adhesion and detachment processes occurring during fibre development. To explore the relationship between fibre quality and the pace at which cotton fibres develop, a structural and compositional analysis of the CFML was carried out in several cultivars with different fibre properties belonging to four commercial species: Gossypium hirsutum, G. barbadense, G. herbaceum and G. arboreum. Results: Cotton fibre cell adhesion, through the cotton fibre middle lamella (CFML), is a developmentally regulated process determined by genotype. The CFML is composed of de-esterified homogalacturonan, xyloglucan and arabinan in all four fibre-producing cotton species: G. hirsutum, G. barbadense, G. herbaceum and G. arboreum. Conspicuous paired cell wall bulges are a feature of the CFML of two G. hirsutum cultivars from the onset of fibre cell wall detachment to the start of secondary cell wall deposition. Xyloglucan is abundant in the cell wall bulges and in later stages pectic arabinan is absent from these regions. Conclusions: The CFML of cotton fibres is re-structured during the transition phase. Paired cell wall bulges, rich in xyloglucan, are significantly more evident in the G. hirsutum cultivars than in other cotton species