text:
import numpy as np
import time
import os
import warnings
import cPickle
import gzip
import glob
from joblib import *
import argparse as opt

import skimage.io
from skimage.exposure import equalize_adapthist, rescale_intensity
from skimage.color import rgb2gray

from stain.he import rgb2he
from encode.VAE import VAE

def preprocess_worker(img, img_name):
    #h, _= rgb2he(img, normalize=True)
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        h = rgb2gray(img)
        h = equalize_adapthist(h)
        h = h.astype('float32')
        h = rescale_intensity(h, out_range=(0.0,1.0))
        h = h.reshape( (np.prod(h.shape),) )
        h -= h.mean()

    return h, img_name

def load_images_crc_subtypes(img_path, tile_size, max_per_subtype=1000,
                             max_per_sample=500, max_total=10000):
    """
    Load a series of RGB images (tiles) from a path of the form:

    img_path/<subtype>/<sample_id>/t<I>/<tile_size>/*.jpg

    where
        <subtype> can be 'a', 'b', 'c', 'd', 'e', 'outlier'
        <sample_id> is a 5-character string
        <I> can ba 1, 2, 3, ... tumor component index in image
        <tile_size> can be "64" or "224"

    Once images are read, the H(aematoxylin) intensity is estimated and stored to an
    array. At the end, the data array is normalized.
    """

    # read images
    imgs = list()
    img_names = list()
    total = 0
    for subtype in ['a', 'b', 'c', 'd', 'e', 'outlier']:
        per_subtype = 0
        sample_paths = glob.glob(img_path+'/'+subtype+'/*')
        for sp in sample_paths:
            per_sample = 0
            tumor_comp_paths = glob.glob(sp+'/*')
            for tcp in tumor_comp_paths:
                tile_paths = glob.glob(tcp+'/'+str(tile_size)+'/*.jpg')
                for img_name in tile_paths:
                    img = skimage.io.imread(img_name)
                    imgs.append(img)
                    img_names.append(img_name)
                    per_sample += 1
                    per_subtype += 1
                    total += 1
                    if per_sample >= max_per_sample or \
                        per_subtype >= max_per_subtype or \
                        total >= max_total:
                        break
                if per_sample >= max_per_sample or \
                    per_subtype >= max_per_subtype or \
                    total >= max_total:
                    break

            if per_subtype >= max_per_subtype or total >= max_total:
                break
        if total >= max_total:
            break

    res = Parallel(n_jobs=cpu_count()) \
        ( delayed(preprocess_worker)(img_, n_) for img_, n_ in zip(imgs, img_names) )

    # deparse results
    # could use:
    # imgs, img_names = zip(*res)
    # imgs = list(imgs)
    # img_names = list(img_names)
    # but it does not look efficient storage-wise for very large lists...
    imgs = [r[0] for r in res]
    img_names = [r[1] for r in res]

    X = np.vstack(imgs)
    X -= X.mean(axis=0)
    X /= X.std(axis=0)

    return X, img_names

def main():
    p = opt.ArgumentParser(description="""
            Encodes a set of images using the Variational Auto-encoder.
            """)
    p.add_argument('img_path', action='store', help='path to image folders')
    p.add_argument('model_path', action='store', help='path where to store the model')
    p.add_argument('enc_num_units', action='store', help='number of hidden units of the encoder', type=int)
    p.add_argument('dec_num_units', action='store', help='number of hidden units of the decoder', type=int)
    p.add_argument('code_size', action='store', help='number of elements in the encoded representation', type=int)
    p.add_argument('-b', '--batch_size', action='store', help='batch size', type=int, default=100)
    p.add_argument('-t', '--tile_size', action='store', help='size of the rectangular tile', type=int, default=64)
    p.add_argument('-s', '--max_per_subtype', action='store', help='maximum sample size per subtype',
                   type=int, default=100)
    p.add_argument('-m', '--max_per_sample', action='store', help='maximum number of tiles per biological sample',
                   type=int, default=100)
    p.add_argument('-n', '--max_total', action='store', help='maximum total sample size',
                   type=int, default=1000)
    p.add_argument('-r', '--learning_rate', action='store', help='learning rate', type=float, default=0.001)
    p.add_argument('-p', '--epochs', action='store', help='number of training epochs',
                   type=int, default=50)
    p.add_argument('-c', '--continuous', action='store_true', help='continuous model?')

    args = p.parse_args()

    print "Loading data..."
    X, imgs_names = load_images_crc_subtypes(args.img_path,
                                             args.tile_size,
                                             args.max_per_subtype,
                                             args.max_per_sample,
                                             args.max_total)
    print "\t --> loaded {0} images of {1} elements". format(X.shape[0], X.shape[1])

    print "Initializing the model..."
    model = VAE(args.continuous,
                args.enc_num_units,
                args.dec_num_units,
                args.code_size,
                X,
                batch_size=args.batch_size,
                learning_rate=args.learning_rate
                )
    print "\t --> done."

    batch_order = np.arange(int(model.N / model.batch_size))
    LB_list = list()

    print "Training..."
    for epoch in np.arange(args.epochs):
        print "\tEpoch {0}: ".format(epoch)
        tic = time.time()
        LB = 0.0
        np.random.shuffle(batch_order)

        for batch in batch_order:
            LB += model.update(batch, epoch)

        LB /= len(batch_order)
        LB_list.append(LB)
        toc = time.time()
        print "\t\t --> finished. LB: {1}, time: {2}".format(epoch, LB, toc-tic)
        #np.save(args.model_path + "LB_list.npy", LB_list)
        #model.save_parameters(args.model_path)
    print("\t --> done.")

    valid_LB = model.likelihood(X)
    print "LB on training set: {0}".format(valid_LB)

if __name__ == '__main__':
    main()

redpajama_set_name: RedPajamaGithub


text:
Vividion Brings in Proven Industry Leader Jeffrey Hatfield as Chief Executive Officer
SAN DIEGO, CA, November 23, 2020 – Vividion Therapeutics, a biotechnology company discovering and developing selective small molecule medicines that drug traditionally inaccessible targets, today announced that Jeffrey Hatfield has been appointed as the company's chief executive officer. Mr. Hatfield brings more than 35 years of successful industry leadership to Vividion, having held multiple senior executive, CEO and board director roles during his career at both Fortune 500 pharmaceutical and emerging biotechnology companies.
"Jeff is a highly accomplished industry leader who can help us deliver the full potential of Vividion's differentiated discovery platform," said Richard Heyman, Ph.D., chairman of the Vividion board of directors. "Jeff brings decades of relevant experience, including building platform-based companies, advancing first-in-class programs, executing financing strategies to drive near and long-term value growth, negotiating innovative business development partnerships and strategic transactions, and cultivating collaborative, high-performance cultures. The board is confident in Jeff's ability to drive significant future growth, and we are thrilled to welcome him to the team."
"Vividion is built on unprecedented insights into emerging and synergistic technologies, which together, create a powerful drug discovery platform with the potential to tremendously expand horizons for small molecule discovery – beyond what we consider possible today," said Mr. Hatfield. "The company stands well positioned for success, with several rapidly advancing firstin-class programs in oncology and immunology, major pharmaceutical collaborations with BMS and Roche, a multi-year cash runway, and the significant backing of top-tier life science investors. I am excited to join this highly talented team as we work together to bring forward important new medicines that have the opportunity to extend and enhance the lives of millions of patients around the world."
Among Mr. Hatfield's many career leadership roles, he previously served as the CEO of Vitae Pharmaceuticals, Inc., a pioneer in computational structure-based drug discovery, where he led the company from start-up to clinical-stage advancement of multiple first-in-class programs, and ultimately to its $640 million acquisition by Allergan Plc. Earlier, he served as a senior executive at Bristol-Myers Squibb (BMS), where he held roles including senior vice president, Immunology and Virology Divisions; president, BMS-Canada; and head of U.S. market access. Mr. Hatfield currently serves as chairman of the board of miRagen Therapeutics, Inc. and as a board member at aTyr Pharma. He also serves as a Key Advisory Board member for the Harvard Business School's Blavatnik Fellowship in Life Science Entrepreneurship, and is a faculty member at Purdue University, where he teaches entrepreneurship to doctoral students. Mr. Hatfield holds an MBA from The Wharton School, University of Pennsylvania, and a B.S. from the Purdue University College of Pharmacy.

redpajama_set_name: RedPajamaCommonCrawl


Place the potato chunks in lightly salted water in a large saucepan over high heat and bring to the boil. Cook for around 10 minutes until the potatoes are cooked all the way through. Drain and set aside to cool.
While the potatoes are cooking, drop the peas in a small saucepan of lightly salted boiling water and cook for 3–4 minutes. Drain and set aside to cool.
In a food processor, gently blitz the seafood, cooled potato, dill, chives, lemon zest, capers, chilli, cooled peas and lemon-infused olive oil, taking care not to overwork the mixture.
Preheat the oven to 120°C and add a small oven-proof saucer of water to the back of the oven to create humidity.
Using your hands, gently roll the mixture into 50 g balls.
Spread four pieces of baking paper out and lay lemon slices and the extra dill down the middle of each piece.
Place three or four balls on each piece of paper, covering the lemon and dill. Add some garlic slices and a small dab of butter on top of each ball.
Fold over the long edges of the baking paper, twisting the ends together to seal the seafood balls in the paper.
Tie off the ends securely with cooking string. Continue the process with the rest of the mixture until you have about four bags for baking.
Cook in the humid oven for 18 minutes.
Serve immediately. Makes 16 meatballs.
Credits: Recipe and Images from Meatballs, The Ultimate Guide, published by Matteo Bruno published by Murdoch Books.
Photo Credits: Recipe and Images from Meatballs, The Ultimate Guide, published by Matteo Bruno published by Murdoch Books.


Can You Sue if You Are Injured by an Uber or a Lyft Driver in Virginia?
We are sometimes asked about liability for crashes involving ride-hailing services and whether a passenger can sue if he or she is injured by an Uber or a Lyft driver in Virginia.
Uber and Lyft ride-hailing services have become increasingly popular in recent years but concerns about liability linger. The drivers are independent contractors presenting difficulties over claims against the companies.
Rides-hailing is a cheaper way of getting around than traditional taxi services. The massive growth of these app-linked companies has likely prevented many drunk drivers from getting in their cars.
The liability issue has been a concern, though. What if you are injured by a negligent Uber driver or by an uninsured motorist who hits the car you are riding in?
Your auto insurance policy excludes coverage for using a car for hire like a taxi service. The Uber driver's policy may not protect you.
Fortunately, a law enacted in 2016 in Virginia attempted to close the loophole by requiring $1,000,000 in coverage for rideshare drivers. Insurance companies are now offering coverage to Uber and Lyft drivers.
Virginia also says Uber and Lyft must abide by a zero-tolerance policy regarding the use of drugs and alcohol by their drivers.
As well as the states, the ride-hailing companies have improved their coverage.
In 2013, for example, Uber added insurance coverage to its UberX service, which uses drivers lacking commercial licenses or commercial insurance. Originally, it only covered drivers after they accepted a ride request and while they were transporting a passenger. Uber expanded its remit to cover drivers during their entire time on duty under certain circumstances. However, an insurance gap remained.
If you have been hurt while taking a ride-hailing service, you should first get medical attention and report the incident to the police.
This remains a very complicated area of personal injury law so you should get legal advice.
A claim should be set up with the Uber or Lyft driver's insurance, the insurance of the other at-fault driver if you were hurt in a multi-vehicle accident, and with your own auto insurance.
Many people now have the apps for ride-sharing services. Fortunately, many of the initial issues regarding a lack of regulations have been ironed out. Other states like North Carolina have also regulated Uber.
If you have been hurt while taking an Uber or Lyft service or your car was hit by a rides-hailing car, you have rights. However, the situation is still complex. It makes sense to call an experienced Hampton Roads car accident injury lawyer at (757) 455-0077.


We will start at the Swantown Inn & Spa, located at 1431 11th Ave SE. Light refreshments such as Hot Chocolate and Cookies will be served. Then we will head off as a group to see the windows around the neighborhood.
Posted December 28, 2017 by Nathan Allan & filed under Events.


text:
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.

using System;
using System.Collections.Generic;
using System.Text.Json;
using System.Threading.Tasks;
using Azure.Storage.Blobs.Models;

namespace Azure.Storage.Blobs.ChangeFeed
{
    internal class SegmentFactory
    {
        private readonly BlobContainerClient _containerClient;
        private readonly ShardFactory _shardFactory;

        /// <summary>
        ///  Constructor for mocking.
        /// </summary>
        public SegmentFactory() { }

        public SegmentFactory(
            BlobContainerClient containerClient,
            ShardFactory shardFactory)
        {
            _containerClient = containerClient;
            _shardFactory = shardFactory;
        }

#pragma warning disable CA1822 // Does not acces instance data can be marked static.
        public virtual async Task<Segment> BuildSegment(
#pragma warning restore CA1822 // Can't mock static methods in MOQ.
            bool async,
            string manifestPath,
            SegmentCursor cursor = default)
        {
            // Models we need for later
            List<Shard> shards = new List<Shard>();
            DateTimeOffset dateTime = BlobChangeFeedExtensions.ToDateTimeOffset(manifestPath).Value;

            // Download segment manifest
            BlobClient blobClient = _containerClient.GetBlobClient(manifestPath);
            BlobDownloadStreamingResult blobDownloadStreamingResult;

            if (async)
            {
                blobDownloadStreamingResult = await blobClient.DownloadStreamingAsync().ConfigureAwait(false);
            }
            else
            {
                blobDownloadStreamingResult = blobClient.DownloadStreaming();
            }

            // Parse segment manifest
            JsonDocument jsonManifest = null;
            try
            {
                if (async)
                {
                    jsonManifest = await JsonDocument.ParseAsync(blobDownloadStreamingResult.Content).ConfigureAwait(false);
                }
                else
                {
                    jsonManifest = JsonDocument.Parse(blobDownloadStreamingResult.Content);
                }

                foreach (JsonElement shardJsonElement in jsonManifest.RootElement.GetProperty("chunkFilePaths").EnumerateArray())
                {
                    string shardPath = shardJsonElement.ToString().Substring("$blobchangefeed/".Length);
                    ShardCursor shardCursor = cursor?.ShardCursors?.Find(x => x.CurrentChunkPath.StartsWith(shardPath, StringComparison.InvariantCulture));
                    Shard shard = await _shardFactory.BuildShard(
                        async,
                        shardPath,
                        shardCursor)
                        .ConfigureAwait(false);
                    if (shard.HasNext())
                    {
                        shards.Add(shard);
                    }
                }
            }
            finally
            {
                jsonManifest?.Dispose();
            }

            int shardIndex = 0;
            string currentShardPath = cursor?.CurrentShardPath;
            if (!string.IsNullOrWhiteSpace(currentShardPath))
            {
                shardIndex = shards.FindIndex(s => s.ShardPath == currentShardPath);
                if (shardIndex < 0)
                {
                    // Either shard doesn't exist or cursor is pointing to end of shard. So start from beginning.
                    shardIndex = 0;
                }
            }
            return new Segment(
                shards,
                shardIndex,
                dateTime,
                manifestPath);
        }
    }
}


The SSUmini is used to filter bicarbonate concentrate and water for dialysis. The filter's compact design provides an economical solution for low flow rates.
Meets and exceeds ANSI/AAMI/ISO 11663:2009 standards.
Up to 12-month filter life.
Designed for low flow situations.


text:
Mechanochemical Synthesis of Defective Molybdenum Trioxide
May 22, 2019 - Bowen Li. Bowen Li. Department of .... Innovation Center for New Energetic Materials, School of National Defense Science. & Technology ...
Polyamine Complexes of Molybdenum Trioxide
THE SEPARATION OF TUNGSTEN TRIOXIDE FROM MOLYBDENUM
Cocondensation products of molybdenum trioxide vapor with
Polyamine Complexes of Molybdenum Trioxide - Inorganic Chemistry
Cocondensation products of molybdenum trioxide ... - ACS Publications
Molybdenum Trioxide Nanostructures: The Evolution from Helical
Research Article pubs.acs.org/journal/ascecg
Cite This: ACS Sustainable Chem. Eng. XXXX, XXX, XXX−XXX
Mechanochemical Synthesis of Defective Molybdenum Trioxide, Titanium Dioxide, and Zinc Oxide at Room Temperature Rui Li,†,‡ Jinhu Wang,† Yi He,*,† Faqin Dong,*,‡ Liang Bian,‡ and Bowen Li§ †
Downloaded by UNIV AUTONOMA DE COAHUILA at 19:34:12:221 on May 24, 2019 from https://pubs.acs.org/doi/10.1021/acssuschemeng.9b00374.
State Key Laboratory of Environment-friendly Energy Materials, Sichuan Co-Innovation Center for New Energetic Materials, School of National Defense Science & Technology, Southwest University of Science and Technology, Mianyang 621010, P. R. China ‡ China Key Laboratory of Solid Waste Treatment and Resource Recycle, Ministry of Education, State Key Laboratory Cultivation Base for Nonmetal Composites and Functional Materials, Southwest University of Science and Technology, Mianyang 621010, P. R. China § Department of Materials Science and Engineering, Michigan Technological University, Houghton 49931, United States S Supporting Information *
ABSTRACT: Defect engineering on metal oxide semiconductors is considered as a versatile approach to regulate their properties. The traditional method for preparation of defective metal oxide semiconductors always requires harsh reaction conditions such as high temperature, high pressure, and explosive chemicals. Here we report an environmentally friendly mechanochemical synthesis way for preparation of several defective metal oxide semiconductors (MoO3, TiO2, and ZnO) in 10 g quantities by grinding semiconductor powders with ascorbic acid. The resulting vacancy-rich TiO2 and ZnO show a strong visible-light absorption. Meanwhile, the obtained hydrogendoped MoO3 (H0.5MoO3) exhibits an exceptional localized surface plasmon resonance (LSPR), which follows Prout-Tompkins B1 solid-state reaction model. In addition, the plasmonic H0.5MoO3 is further utilized for colorimetric detection of H2O2. This colorimetric assay is able to determine concentrations of H2O2 from 0.05 to 3 mM with a detection limit of 30 μM. It can distinguish the presence of 3 mM H2O2 by the naked eye. KEYWORDS: Mechanochemical synthesis, Defect, Localized surface plasmon resonance, Colorimetry, Hydrogen peroxide
INTRODUCTION Defect engineering in metal oxide semiconductors has been demonstrated as an effective route to tune their performances, because the defect structures of metal oxide semiconductors strongly affect their fundamental chemical and physical properties as well as practical applications.1−15 For instance, molybdenum trioxide (MoO3), a stable n-type metal oxide semiconductor with a bandgap of 3.2 eV, can show metallic behavior when the detects such as hydrogen doping are introduced because of the increase of free carrier concentration.16−20 As a result, the defective MoO3 exhibits localized surface plasmon resonance (LSPR) which are widely investigated in noble metal nanomaterials.21,22 Additionally, the introduction of vacancy-oxygen defects into TiO2 is able to enhance its visible light absorption as well as photocatalytic activity.23 To date, several approaches have been developed to prepare defective MoO3, TiO2, and ZnO. Recently, Yamashita's group reported an H-spillover route to prepare hydrogen doped MoO3 with strong LSRP property by Pd catalytic hydrogenation at 300 °C for 2 h.24 Zhang et al. developed a hydrothermal method for synthesis HxMoO3 at 180 °C for 3 h using tartaric acid as the reducing agent.25 However, these methods involve harsh reaction conditions, © XXXX American Chemical Society
including high temperature, high pressure, and explosive chemicals. As an alternative to traditional chemical preparation in solution, mechanochemical synthesis by grinding or milling has emerged as an environmentally friendly approach. It offers cleaner, more efficient, faster, and solvent-free reaction environment for a variety of transformations, including drug cocrystals, nanomaterials, metal−organic frameworks, covalent organic frameworks, metal nitrides, and metathesis.26−30 Despite its popularity and wide application, mechanochemical synthesis of defective MoO3, TiO2, and ZnO is not reported in the literature to the best of our knowledge. Herein, we develop a simple, effective, rapid, scalable method for synthesis of defective metal oxide semiconductors in gram quantities by directly grinding semiconductor powders with ascorbic acid (Figure 1). Several metal oxide semiconductors, including MoO3, TiO2, and ZnO, are doped with hydrogen atoms or oxygen vacancies. Besides, the resulting hydrogen doped MoO3 shows exceptional LSPR, which is Received: January 21, 2019 Revised: April 11, 2019
DOI: 10.1021/acssuschemeng.9b00374 ACS Sustainable Chem. Eng. XXXX, XXX, XXX−XXX
ACS Sustainable Chemistry & Engineering
Figure 1. Schematic of the mechanochemical synthesis of defective metal oxide semiconductors.
applied to visually determine hydrogen peroxide. And the oxygen vacancy-rich TiO2, and ZnO display an optical absorption characteristic in the visible-light region.
Figure 2. Pristine and defective metal oxide semiconductors. (a) Photographs and XRD patterns of pristine and defective (b) MoO3, (c) TiO2, and (d) ZnO.
EXPERIMENTAL SECTION
Chemicals. MoO3 powder, hydrogen peroxide, and inorganic salts were purchased from ChengDu KeLong Co., Ltd. TiO2 nanopowder, ZnO powder, L-ascorbic acid, L-glutathione, L-cysteine hydrochloride monohydrate, D-glucose, and α-lactose monohydrate were obtained from Aladdin Chemical Co., Ltd. All of the chemicals were used as received without further purification. The deionized water (DI, 18.2 MΩ·cm) was used to prepare various solutions. Synthesis of Defective Metal Oxide Semiconductors. A total of 10 g of metal oxide semiconductors and a certain amount of Lascorbic acid as the reducing agents were put in a mortar, followed by grinding for 1 h using a pestle at room temperature. The resulting powders were dispersed into DI water. After centrifuging and washing with DI water, the resulting defective metal oxide semiconductor was dried at room temperature for further characterization. Procedure of Colorimetric Detection of H2O2. Typically, a series of 2.3 mL H2O2 solutions with different concentrations were added to several 5 mL glass vials. Subsequently, 0.7 mL of hydrogen doped MoO3 (H0.5MoO3) dispersion was injected to each vial. After 10 min incubation at room temperature, the UV−vis absorption and the corresponding photographs were collected by UV−vis spectrometer and camera, respectively. Characterization. UV−vis absorption spectra were obtained from absorption spectrophotometer (UV-1800, Shimadzu). Scanning electron microscope (SEM) images were recorded with MAIA3LMU scanning electron microscopy (TESCAN). X-ray diffraction (XRD) patterns were taken on an X Pert pro X-ray diffractometer. X-ray photoelectron spectroscopy (XPS) spectra were carried out performed on a Thermo Escalab 250Xi spectrophotometer. Fourier transform infrared (FTIR) spectra were conducted on a TENSOR spectrophotometer (Bruker).
(Figure 2b). It can be seen that the resulting H0.5MoO3 only shows few XRD peaks. The reason is that the interaction between water and H0.5MoO3 induces the exfoliation of H0.5MoO3 to generate two-dimensional H0.5MoO3 nanosheet, which damages the three-dimensional structure of H0.5MoO3 during the dissolution process for separation and purification, leading to the disappearance of the diffraction peaks from three-dimensional diffraction lines (Figure S1). In order to prove the generality of this mechanochemical synthesis method, we employ the same strategy to synthesize defective TiO2 and ZnO. Similarly, the distinct color changes from white to yellow are observed in the ascorbic acid-treated TiO2 and ZnO (Figure 2a). As displayed in Figure 2c,d, the XRD patterns of TiO2 and ZnO after reaction do not change, demonstrating that the mechanochemical reaction does not alter their intrinsic crystal structures. Accordingly, the color change is ascribed to be the fact that the TiO2 and ZnO are able to be partially reduced to generate oxygen vacancy-rich TiO2 and ZnO (TiO2‑x and ZnO1−x), which increases the visible light absorption.31,32 Meanwhile, this mechanochemical synthesis approach is easily used for scalable preparation of defective metal oxide semiconductors in ten-gram quantities as shown in Figure S2. The morphology of MoO3 and H0.5MoO3 is investigated by SEM. As depicted in Figure 3a, the MoO3 have microsized plates. When it is transformed into H0.5MoO3, the layer structure is well preserved (Figure 3b), implying successful hydrogen doping without breaking the inherent structure. To confirm the doping of hydrogen into MoO3, we perform XPS measurements as presented in Figure 3c,d. The high-resolution Mo 3d XPS spectra of MoO3 reveals two characteristic binding energy peaks at 233.3 and 236.4 eV, originating from 3d5/2 and 3d3/2 of Mo6+ species (Figure 3c).32,34 Hydrogen doping causes the Mo 3d XPS spectra to broaden, which can be fitted into a set of doublet peaks (Figure 3d). Except for Mo6+, two peaks at 232.2 and 234.8 eV corresponding to 3d5/2 and 3d3/2 of Mo5+ are found, indicating that the hydrogen doping induces the partial reduction of MoO3. Also, the morphology, structure, and surface composition of TiO2‑x and ZnO1−x are
RESULTS AND DISCUSSION Preparation and Characterization of Defective Metal Oxide Semiconductors. To prepare defective MoO3, TiO2, and ZnO, ascorbic acid was chosen as the reductant. As shown in Figure 2a, the pristine MoO3 has a pale cyan color. However, after grinding with ascorbic acid for 1 h, the resulting MoO3 samples exhibit a blue color, suggesting that a chemical transformation occurs. The XRD peaks for ascorbic acidtreated MoO3 samples can be indexed to the orthorhombic H0.5MoO3 (JCPDS No. 14−0041), indicating that hydrogen atoms are successfully doped into the crystal of H0.5MoO3 B
shows a blue color and strong optical absorption from visible to near-infrared region with an absorption peak at 775 nm (Figure 3f). The absorption peak of H0.5MoO3 dispersion is owing to LSPR.24,36 Kinetic Analysis for Mechanochemical Synthesis of H0.5MoO3. The exceptional LSPR absorption of H0.5MoO3 allows us to analyze the mechanochemical process. Figure 4a
Figure 3. Structural characterization for the H0.5MoO3. (a, b) SEM images, (c,d) high-resolution Mo 3d XPS spectra, (e) FT-IR spectra, and (f) UV−vis absorption spectra of MoO3 and H0.5MoO3 dispersion.
Figure 4. Time-resolved monitoring of the production of H0.5MoO3. (a) Successive UV−vis absorption spectra of mechanochemical synthesis of H0.5MoO3. (b) The fitting of conversion fraction (α) as the function of reaction time for B1 solid-state reaction model.
studied by SEM as shown in Figure S3−S10. The results confirm the successful preparation of TiO2‑x and ZnO1−x. Intuitively, the surface of H0.5MoO3 from the ascorbic acidtreated MoO3 may contain ascorbic acid molecules because Mo atoms can coordinate with hydroxyl groups of ascorbic acid. To validate it, we use FTIR spectroscopy to characterize the surface composition of H0.5MoO3. The FTIR spectrum of MoO3 displays two peaks at 995 and 870 cm−1 (Figure 3e), corresponding to stretching vibrations of the terminal Mo = O and Mo−O−Mo bridging bonds.35 There is no absorption peak of MoO3 in the wavenumber range of 3000−1000 cm−1. In contrast, the FTIR spectrum of H0.5MoO3 shows additional absorption peaks at 3423, 2929, 1679, and 1095 cm−1, which are attributed to the stretching vibrations of O−H, C−H, C C, and C−O bonds of ascorbic acid, respectively. The results affirm the presence of ascorbic acid on the surface of H0.5MoO3, which enables its water-solubility. Likewise, the resulting TiO2‑x and ZnO1−x are also water-soluble as show in Figures S11 and S12. Apart from ascorbic acid, other mild reducing agents such as glutathione (GSH), cysteine (Cys), Dglucose (Glu), and α-lactose (Lac) are used for preparation of H0.5MoO3. Nevertheless, the UV−vis absorption of MoO3 treated with these substances are much lower than that of ascorbic acid-treated MoO3 (Figure S13). This suggests that the ascorbic acid is an ideal agent for synthesis of H0.5MoO3. It should be noted that AA is able to reduce the MoO3 dispersion to form oxygen-vacancy-rich MoO3 (MoO3‑x) nanosheet in the solution state with the assistance of the strong acid (pH 2).33,34 However, here AA directly reacts with MoO3 to generate H0.5MoO5 rather than MoO3‑x, suggesting that the reaction of AA and MoO3 undergoes different pathways in the solution and solid states, respectively. The present H0.5MoO3 dispersion
depicts the successive UV−vis absorption spectra for the generation of plasmonic H0.5MoO3. We fit the time dependence of the conversion fraction (α) to ten solid-state reaction mathematical models, including Prout-Tompkins B1, AvramiErofe'ev models (A2, A3, and A4), geometrical contraction models (R2 and R3), and diffusion models (D1, D2, D3, and D4) as shown in Figures 4b and S14.24,37 It can be seen that the best fit is obtained using B1 model (Figure 4b), testifying that this reaction for mechanochemical synthesis of H0.5MoO3 kinetically follows an autocatalysis model. The reason may be that the resulting water promotes the ascorbic acid dissociation to create hydrogen ions, which is beneficial to the hydrogen-doping reaction pathway. As well, we fit the conversion fraction data to a general Johnson-MehlAvrami−Kolmogorov (JMAEK) equation to determine the rate constant (k) as k = 1.4 × 10−3 min−1 (Figure S15).38 Visual Colorimetric Detection of H2O2 Based on Plasmonic H0.5MoO3. Based on the LSPR absorption ofH0.5MoO3 dispersion, the H0.5MoO3 provides a platform for visual colorimetric detection of oxidants. The introduction of oxidants is capable of oxidizing Mo5+ to form Mo6+, which decreases the surface free carrier concentration and therefore diminishes LSRP absorption.39,40 As a demonstration, H2O2 is selected as the model analyte because it possess an important biological function. Exposure of H0.5MoO3 dispersion to H2O2 results in the absorbance decrease at 775 nm (Figure 5a) because the oxidation process degenerates hydrogen and reduces the free carrier concentration on the surface of H0.5MoO3. As presented in Figure 5b, the absorbance of H0.5MoO3 dispersion at 775 nm gradually decreases upon increasing H2O2 concentration. Moreover, a strong linear C
process follows B1 solid reaction model. Furthermore, the resulting H0.5MoO3 dispersion is successfully employed for visual colorimetric detection of H2O2 as well. The present new mechanochemical synthesis approach for metal oxide semiconductors has significant advantages, such as universality, simpleness, high efficiency, and scalability. The obtained defective MoO3, TiO2, and ZnO will find more efficient and important applications in photocatalysis, biosensing, drug delivery, energy storage and conversion, and so forth.
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acssuschemeng.9b00374.
Figure 5. Detection performance of H0.5MoO3 dispersion. (a) UV− vis absorption spectra of H0.5MoO3 dispersion in the absence and presence of H2O2. (b) UV−vis absorption spectra of H0.5MoO3 dispersion in the presence of different concentrations of H2O2. (c) The logarithm of absorbance change (A0−A) as a function of H2O2 concentrations in the range of 0.05−3 mM, where A0 and A are the absorbance values of H0.5MoO3 dispersion at 775 nm in the absence and presence of H2O2, respectively. (d) Colorimetric responses of H0.5MoO3 dispersion toward H2O2 and various potential interferents.
Optical and SEM images, UV−vis absorption spectra, FTIR spectra, and XPS spectra (Figures S1−S18) (PDF)
*E-mail: [email protected] *E-mail: [email protected] ORCID
Yi He: 0000-0002-9324-3305 Liang Bian: 0000-0002-2769-7018 Author Contributions
R.L. carried out the experiments. R.L. and J.W. drew these illustrations. B.L. interpreted the data. F.D. and L.B. provided reagents/materials/analysis tools. Y.H. interpreted the data and wrote the manuscript. All authors reviewed the manuscript.
relationship is obtained between the logarithm of absorbance change and H2O2 concentration spanning from 0.05 to 3 mM (Figure 5c). The limit of detection (LOD) is about 30 μM at the signal-to-noise ratio of 3, which is comparable to that of the reported colorimetric assay.41,42 Interestingly, an apparent color change from blue to colorless is observed when the detection system contains 3 mM H2O2, which can be utilized for visual detection of H2O2 by the naked eye. Notably, the present colorimetric assay manifests an appreciable selectivity toward H2O2. The introduction of a series of potential interferents, including small organic molecules (dithiothreitol (DTT), glucose (Glu), threonine (Thr), and sucrose (Suc)), reductive polymer (polyethylene glycol (PEG)), as well as conventional inorganic ions (Mg2+, Mn2+, Zn2+, Na+, K+, NH4+, Cl−, Br−, NO3−, SO42−, SO32−, and SO32−), leads to a negligible colorimetric response (Figure 5d). Certainly, when the oxidants that have stronger oxidation property than that of H2O2 such as NaClO and KMnO4, they will result in apparent absorbance change at 775 nm (Figure S16). However, these oxidants cannot coexist with H2O2 because they are easy to react with H2O2 by redox reaction. What is more, the resulting ZnO1−x and TiO2‑x can also respond to oxidants such as H2O2, KMnO4, and Fe3+ as shown in Figures S17 and S18.
The authors declare no competing financial interest.
ACKNOWLEDGMENTS The support of this research by the National Natural Science Foundation of China (21705134, 41872039, and 41831285), One-Thousand-Talents Scheme in Sichuan Province, Sichuan Science and Technology Program (2018JY0462), and Longshan academic talent research supporting program of SWUST (Grant No. 18LZX204 and 17LZX449) is gratefully acknowledged.
(1) Bai, S.; Zhang, N.; Gao, C.; Xiong, Y. J. Defect engineering in photocatalytic materials. Nano Energy 2018, 53, 296−336. (2) van de Walle, C. G. Hydrogen as a cause of doping in zinc oxide. Phys. Rev. Lett. 2000, 85 (5), 1012−1015. (3) Nowotny, J.; Alim, M. A.; Bak, T.; Idris, M. A.; Ionescu, M.; Prince, K.; Sahdan, M. Z.; Sopian, K.; Teridi, M. A. M.; Sigmund, W. Defect chemistry and defect engineering of TiO2-based semiconductors for solar energy conversion. Chem. Soc. Rev. 2015, 44 (23), 8424−8442. (4) Yue, Y. L.; He, Y. A sensitive and selective method for visual chronometric detection of copper (II) ions using clock reaction. Anal. Sci. 2019, 35 (2), 159−163. (5) Ramesh, R. Defect engineering using crystal symmetry. Proc. Natl. Acad. Sci. U. S. A. 2018, 115 (38), 9344−9346. (6) Wang, H.; Zhang, J. J.; Hang, X. D.; Zhang, X. D.; Xie, J. F.; Pan, B. C.; Xie, Y. Half-metallicity in single-layered manganese dioxide nanosheets by defect engineering. Angew. Chem., Int. Ed. 2015, 54 (4), 1195−1199.
CONCLUSIONS In conclusion, we have developed a mechanochemical synthesis avenue for environmentally friendly preparation of defective MoO3, TiO2, and ZnO at room temperature. Our investigations reveal that the H0.5MoO3 and vacancy-rich TiO2 and ZnO in 10 g quantities can be easily obtained by grinding with ascorbic acid. The H0.5MoO3 dispersion displays unique LSPR absorption with a blue color. Simultaneously, the enhanced visible-light absorption from defective TiO2 and ZnO is found. The kinetic analysis suggests that the production D
ACS Sustainable Chemistry & Engineering (7) Zhao, M. X.; Tao, Y.; Huang, W.; He, Y. Reversible pH switchable oxidase-like activities of MnO2 nanosheets for a visual molecular majority logic gate. Phys. Chem. Chem. Phys. 2018, 20 (45), 28644−28648. (8) Zhou, Y.; Huang, W.; He, Y. pH-Induced silver nanoprism etching-based multichannel colorimetric sensor array for ultrasensitive discrimination of thiols. Sens. Actuators, B 2018, 270, 187−191. (9) Zhang, N.; Li, X. Y.; Ye, H. C.; Chen, S. M.; Ju, H. X.; Liu, D. B.; Lin, Y.; Ye, W.; Wang, C. M.; Xu, Q. Oxide defect engineering enables to couple solar energy into oxygen activation. J. Am. Chem. Soc. 2016, 138 (28), 8928−8935. (10) Zhao, M. X.; Yu, H. L.; He, Y. A dynamic multichannel colorimetric sensor array for highly effective discrimination of ten explosives. Sens. Actuators, B 2019, 283, 329−333. (11) Li, Z.; Xiao, C.; Zhu, H.; Xie, Y. Defect chemistry for thermoelectric materials. J. Am. Chem. Soc. 2016, 138 (45), 14810− 14819. (12) Liu, X.; You, B.; Yu, X. Y.; Chipman, J.; Sun, Y. Electrochemical oxidation to construct a nickel sulfide/oxide heterostructure with improvement of capacitance. J. Mater. Chem. A 2016, 4 (30), 11611− 11615. (13) Huang, W.; Deng, Y.; He, Y. Visual colorimetric sensor array for discrimination of antioxidants in serum using MnO2 nanosheets triggered multicolor chromogenic system. Biosens. Bioelectron. 2017, 91, 89−94. (14) You, B.; Zhang, Y.; Yin, P.; Jiang, D. E.; Sun, Y. Universal molecular-confined synthesis of interconnected porous metal oxidesNC frameworks for electrocatalytic water splitting. Nano Energy 2018, 48, 600−606. (15) You, B.; Li, N.; Zhu, H.; Zhu, X.; Yang, J. Graphene oxidedispersed pristine CNTs support for MnO2 nanorods as high performance supercapacitor electrodes. ChemSusChem 2013, 6 (3), 474−480. (16) Agrawal, A.; Johns, R. W.; Milliron, D. J. Control of localized surface plasmon resonances in metal oxide nanocrystals. Annu. Rev. Mater. Res. 2017, 47, 1−31. (17) Du, J. Y.; Zhao, M. X.; Huang, W.; Deng, Y. Q.; He, Y. Visual colorimetric detection of tin (II) and nitrite using a molybdenum oxide nanomaterial-based three-input logic gate. Anal. Bioanal. Chem. 2018, 410 (18), 4519−4526. (18) Mattox, T. M.; Ye, X.; Manthiram, K.; Schuck, P. J.; Alivisatos, A. P.; Urban, J. J. Chemical control of plasmons in metal chalcogenide and metal oxide nanostructures. Adv. Mater. 2015, 27 (38), 5830− 5837. (19) Huang, W.; Wang, J. H.; Du, J. Y.; Deng, Y. Q.; He, Y. Contrary logic pairs and circuits using a visually and colorimetrically detectable redox system consisting of MoO3‑x nanodots and 3, 3′-diaminobenzidine. Microchim. Acta 2019, 186 (2), 79. (20) Agrawal, A.; Cho, S. H.; Zandi, O.; Ghosh, S.; Johns, R. W.; Milliron, D. J. Localized Surface Plasmon Resonance in Semiconductor Nanocrystals. Chem. Rev. 2018, 118 (6), 3121−3207. (21) Yu, H. L.; Long, D. Y.; Huang, W. Organic antifreeze discrimination by pattern recognition using nanoparticle array. Sens. Actuators, B 2018, 264, 164−168. (22) He, Y.; Xu, B.; Li, W. H.; Yu, H. L. Silver nanoparticle-based chemiluminescent sensor array for pesticide discrimination. J. Agric. Food Chem. 2015, 63 (11), 2930−2934. (23) Ou, G.; Xu, Y. S.; Wen, B.; Lin, R.; Ge, B. H.; Tang, Y.; Liang, Y. W.; Yang, C.; Huang, K.; Zu, D. Tuning defects in oxides at room temperature by lithium reduction. Nat. Commun. 2018, 9 (1), 1302. (24) Cheng, H. F.; Wen, M. C.; Ma, X. C.; Kuwahara, Y.; Mori, K.; Dai, Y.; Huang, B. B.; Yamashita, H. Hydrogen Doped Metal Oxide Semiconductors with Exceptional and Tunable Localized Surface Plasmon Resonances. J. Am. Chem. Soc. 2016, 138 (29), 9316−9324. (25) Zhang, B. Y.; Zavabeti, A.; Chrimes, A. F.; Haque, F.; O'Dell, L. A.; Khan, H.; Syed, N.; Datta, R.; Wang, Y. C.; Chesman, A. S. R. Degenerately Hydrogen Doped Molybdenum Oxide Nanodisks for Ultrasensitive Plasmonic Biosensing. Adv. Funct. Mater. 2018, 28 (11), 1706006.
(26) Biswal, B. P.; Chandra, S.; Kandambeth, S.; Lukose, B.; Heine, T.; Banerjee, R. Mechanochemical synthesis of chemically stable isoreticular covalent organic frameworks. J. Am. Chem. Soc. 2013, 135 (14), 5328−5331. (27) Frišcǐ ć, T.; Halasz, I.; Beldon, P. J.; Belenquer, A. M.; Adams, F.; Kimber, S. A.; Honkimäki, V.; Dinnebier, R. E. Real-time and in situ monitoring of mechanochemical milling reactions. Nat. Chem. 2013, 5 (1), 66−73. (28) Xu, C. P.; De, S.; Balu, A. M.; Ojeda, M.; Luque, R. Mechanochemical synthesis of advanced nanomaterials for catalytic applications. Chem. Commun. 2015, 51 (31), 6698−6713. (29) Crawford, D. E.; Casaban, J. Recent developments in mechanochemical materials synthesis by extrusion. Adv. Mater. 2016, 28 (27), 5747−5754. (30) Jin, T.; Sang, X. H.; Unocic, R. R.; Kinch, R. T.; Liu, X. F.; Hu, J.; Liu, H. L.; Dai, S. Mechanochemical-Assisted Synthesis of HighEntropy Metal Nitride via a Soft Urea Strategy. Adv. Mater. 2018, 30 (23), 1707512. (31) Chen, X. B.; Liu, L.; Yu, P. Y.; Mao, S. S. Increasing Solar Absorption for Photocatalysis with Black Hydrogenated Titanium Dioxide Nanocrystals. Science 2011, 331 (6018), 746−750. (32) Ullattil, S. G.; Narendranath, S. B.; Pillai, S. C.; Periyat, P. Black TiO2 Nanomaterials: A Review of Recent Advances. Chem. Eng. J. 2018, 343, 708−736. (33) Li, R.; An, H. J.; Huang, W.; He, Y. Molybdenum oxide nanosheets meet ascorbic acid: Tunable surface plasmon resonance and visual colorimetric detection at room temperature. Sens. Actuators, B 2018, 259, 59−63. (34) Li, M.; Huang, X.; Yu, H. A colorimetric assay for ultrasensitive detection of copper (II) ions based on pH-dependent formation of heavily doped molybdenum oxide nanosheets. Mater. Sci. Eng., C 2019, 101, 614−618. (35) Hirata, T. In-situ observation of Mo-O stretching vibrations during the reduction of MoO3 with hydrogen by diffuse reflectance FTIR spectroscopy. Appl. Surf. Sci. 1989, 40, 179−181. (36) Manthiram, K.; Alivisatos, A. P. Tunable localized surface plasmon resonances in tungsten oxide nanocrystals. J. Am. Chem. Soc. 2012, 134 (9), 3995−3998. (37) Khawam, A.; Flanagan, D. R. Solid-State Kinetic Models: Basics and Mathematical Fundamentals. J. Phys. Chem. B 2006, 110 (35), 17315−17328. (38) Weinberg, M. C.; Birnie, D. P., III; Shneidman, V. A. Crystallization kinetics and the JMAK equation. J. Non-Cryst. Solids 1997, 219, 89−99. (39) Cheng, H. F.; Qian, X. F.; Kuwahara, Y.; Mori, K.; Yamashita, H. A Plasmonic Molybdenum Oxide Hybrid with Reversible Tunability for Visible-Light-Enhanced Catalytic Reactions. Adv. Mater. 2015, 27 (31), 4616−4621. (40) Huang, W.; Zhou, Y.; Du, J. Y.; Deng, Y. Q.; He, Y. Versatile visual logic operations based on plasmonic switching in label-free molybdenum oxide nanomaterials. Anal. Chem. 2018, 90 (3), 2384− 2388. (41) Liu, Q. Y.; Yang, Y. T.; Li, H.; Zhu, R. R.; Shao, Q.; Yang, S. G.; Xu, J. J. NiO nanoparticles modified with 5,10,15,20-tetrakis(4carboxyl pheyl)-porphyrin: Promising peroxidase mimetics for H2O2 and glucose detection. Biosens. Bioelectron. 2015, 64, 147−153. (42) Sun, L. F.; Ding, Y. Y.; Jiang, Y. L.; Liu, Q. Y. Montmorilloniteloaded ceria nanocomposites with superior peroxidase-like activity for rapid colorimetric detection of H2O2. Sens. Actuators, B 2017, 239, 848−856.


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How to Change BPP University Transcript Marks? Fake Diploma
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There is a way to hold gold and precious metals related investments and get paid while you wait. I have been buying a closed end ETF on the TSX that trades with the symbol MMP.UN. It normally trades at a 15% premium to NAV (Net Asset Value) but in April they issued a warrant offering which has caused its units to drop significantly. These warrants expire on July 23, 2010. The fund last traded (May 7, 2010) at $7.78 per unit but has a fully diluted Net Asset Value of $8.09 at the close of trading on May 7, 2010. The manager pays a $1.20 per year cash distribution ($0.10 per month) resulting in a yield of 15.4%. The yield is expected to be paid from realized capital gains on its holdings.
As long as precious metals and mining stocks are in a secular bull market this funds payouts are sustainable. However, it is very volatile.
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These guys used to be liberal. Then things changed.
It's 1979, and the Southern Baptist Convention isn't that terribly different from most mainstream Christian denominations: since the '50s, it's become increasingly liberal, embracing modern scholarship and social stances while moving away from Biblical inerrancy.
That summer, in Houston, everything changes. Fundamentalists, angered by what they see as repeated betrayals of the church's founding principles, bus in supporters. Instead of a genteel administrative process, they've treated the SBC apparatus the way a political machine does; organizing relentlessly with an eye to seizing as much power as possible.
It works. The moderates and liberals are routed in an "orchestration from the sky boxes." With their victory, the fundamentalists gain access to thousands of churches to spread their views, along with control over universities, media, built-in loyalty and a budget to use for whatever else they want to do. The SBC has remained conservative to this day, and the 1979 takeover played a major role in making the religious right-wing a political force. With the SBC in their grasp, fundamentalists had a foundation to back the efforts of previously scattered fringe groups.
It's 1896, and the Democratic Party is largely a conservative organization, dominated by the "Bourbon" wing and Southern aristocrats; there hasn't really been a major liberal faction of the party since the 1830s. President Grover Cleveland is such a perfect Gilded Age fat cat that Ayn Rand will later swoon over him.
But things are changing. The Populists have made gains throughout the Midwest, including voting friendlier Democrats into office on fusion tickets. While much of the rest of the party is divided, a young congressman, William Jennings Bryan, spends the year-and-a-half leading up to the convention getting to know the delegates and leading the populists to control the endless ground-level meetings that the party's elite assume will turn out in their favor. Instead, Bryan and his allies aimed, in his blunt words, to "organize and take charge of and control the policy" of the party. That summer, hundreds of new delegates pour into Chicago. "They are assembled now," Bryan thundered. "Not to discuss, not to debate, but to enter up the judgment rendered by the plain people of this country."
He rallies them with a hell of a speech and the left wing takes control of the Democratic institutions. The Dems don't take the presidency, but the populists still remake a major political party. While its power has waxed and waned, since 1896 the Democrats have always had to deal with a progressive faction in their midst. The Bourbons were furious — they considered the populists an illness — but the virus had already taken hold.
Then there's the NRA in 1977, the Texas Board of Education, or the founding of the Republican Party. The list goes on.
An institution is, at heart, just a name and a structure attached to resources. That's it, and it means that determined groups of people can infect them until they turn into something very different.


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David @ 360 Degree Fitness: Are you drinking your calories?
One of the first things that I have clients do when they start with me and are keen to lose weight is complete a food diary for around a week. This accomplishes a couple of different things.
Firstly, it shows me what their commitment level is like - keeping a food diary may be a bit of a pain to do, but it isn't that taxing in reality. It takes a few minutes per meal. If it is too much hassle to do this, how ready are they to adjust their lifestyle and keep working on it when things get tough?
That may sound a little pessimistic but I have found a pretty good correlation between the two. Motivation plays a huge role in lifestyle change.
Next, it makes people more mindful about what they are consuming. I make a point of asking them not to alter their eating. It's not about judging them, it's simply a snapshot of where they are starting from. Somewhere to begin, and something that shows me where we could make simple changes that could greatly impact them in the long-term.
One thing that surprises people when they look back through their diary is how many calories they are drinking per day. Many clients work in an office, which to be honest is pretty common for the population as a whole. Often this comes with meetings, morning and afternoon coffees often included, lunches in cafes with friends or coworkers, and after work drinks on Fridays.
One thing to consider if you are trying to lose weight is the amount of calories in those coffees and after work drinks. Especially if those coffees are hot chocolates or mochas.
According to the food diary app that I use (Easy Diet Diary), a regular flat white of 275ml (just over a regulation cup) is 637 kilojoules (kilojoules are just a different energy measurement to calories). Now considering that I have a daily intake of around 8500kj, that makes that coffee about 7.5% of my daily food intake, which is a fairly large amount when you think about it.
And that is just a flat white, the numbers go even higher for hot chocolates and mochas.
Multiply that number if you have a couple of coffees a day and that is 15%. Take into account if you drink anything other than water or calorie free soft drinks with your lunch or at your desk and the numbers start to add up quickly.
Alcohol is the same. Take red wine - generally considered one of the better alcoholic drinks for you (if you can really say that). A 150ml glass of red wine is around 480kj or 5.5% of my available food intake for the day. Add a couple of those after work each Friday, add those to your twice daily coffee and that's around 10,000kj, or about 17% of my weekly food intake.
Now I know that I've thrown a lot of numbers around here. I'm not trying to confuse you. But just get you to think a little more deeply about how you are apportioning your calories.
Many people drink coffee out of habit - I know that I definitely do. Plus you can't really beat that first morning coffee in my opinion! I am not saying not to drink coffee at all, or to abstain completely from drinking alcohol, but look at possible ways to modify your habits to help you achieve your goals.
One thing that I have started doing recently is to swap my morning coffee to a long black with a splash of cold trim milk. This drops my morning coffee from 637kj down to 73kj. That's a huge drop in the amount of kilojoules I am having.
The same can be done with alcohol. If you usually have spirits mixed with soft drinks (bourbon and coke for example), check and see if the bar has sugar free or low calorie soft drinks and get them mixed with your favourite spirit. Or even go to soda water for even less calories.
Again, I am not saying drop these things out of your diet completely - you can try if you want, but I have found that simple changes work well enough if we think about what we are doing and consciously make better decisions.
It's just a matter experimenting until you find out what works best for you.
I'd love to know what tips you have that have worked for you in terms of substituting healthier options into your diet. Let me know through my website.


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Sports medicine involves diagnosing, treating and preventing injuries during sports activity and exercises. Sports injuries can result from accidents, inadequate training, improper use of protective devices, or insufficient stretching or warm-up exercises. The most common sports injuries are sprains and strains, fractures and dislocations.
Physical therapy: exercises and physical manipulation to strengthen muscles and joints, and improve range of motion. Physical therapy can be indicated individually or following surgical treatment.
Make sure that you follow warm up and cool down exercises before and after sports activities. Exercises will help to stretch the muscles, increase flexibility, and reduce soft tissue injuries.
Avoid exercising immediately after eating a large meal.
Maintain a healthy diet to nourish the muscles.
Avoid playing when you are injured or tired. Take a break for some time after playing.
Learn all the rules of the game you are participating in.
Ensure that you are physically fit to play the sport.


We know choosing an advisor isn't easy.
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The Compliance Center is a unique end-to-end compliance solution for contact centers, bringing together the abilities to visualize data and gain privacy focused insights, take actions on policies, and bridge siloes between data systems to efficiently manage interactions and policies. With the GDPR solution, users obtain actionable intelligence on their GDPR related activities and can directly take proactive or corrective actions to ensure that their practices are aligned with the principles of the regulation.
The Compliance Center offers an innovative "Compliance Assurance" application displaying multiple dashboards to gauge the adherence of the organization to the best data governance practices for capture and retention. Whether organizations need to verify that all data concerned is adequately encrypted, prevent breaches by detecting abnormal behavior, verify that consent has been recorded and is discoverable, or track the quantity and the quality of the data stored, the Compliance Center delivers a thorough solution.
By applying thresholds onto the aggregated data, users can specifically focus on the policies, groups or campaigns that include or process private data and effectively monitor these activities so as to detect vulnerabilities. They can also accommodate the requests of their customers to delete their data or obtain a copy of the latter. With the "Policy Manager" application, which centralizes all retention policies and comprises mission-critical mechanisms to manage the data archived, authorized users can take direct action and dramatically reduce the time needed to detect breaches or delete/extract specific interactions.
To accommodate requests to exercise the Right to Be Forgotten, the Compliance Center makes use of advanced data tagging which can retrieve the data associated with any customer ID. It also comprises dedicated workflows for authorized users to perform deletions and/or extractions without the need for 3rd party or professional services. As such, these actions are performed promptly and in strict adherence with the organizations procedures for policy enforcement.
Based on the market leading Engage platform, the Compliance Center provides an end-to-end media encryption approach protecting information during every stage of its lifecycle: capture, use, transmission, and storage. Encryption can also be performed on historical interactions, as a corrective measure.
Overall the Compliance Center offers a single solution for GDPR compliance and a wealth of features and applications that address the needs of all stakeholders in the Call Center – IT, agents, and compliance, in order to power the adoption of a "compliance by design" approach without creating additional overheads.
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Meet John Compoly who uses the Compliance Center to make sure he stays on top of the GDPR.


SAN FRANCISCO, CA--(Marketwire - October 22, 2009) - Transformational private equity firm Exigen Capital today announced that Bruno Bonati has been named to the board of directors of the fund's European advisory company Exigen Capital Europe AG. Bonati, a professional board member and independent IT consultant with a focus on the European banking and insurance industries, brings to Exigen Capital over 23 years of Financial Services and IT experience.
"Particularly in the wake of the economic downturn, we expect IT solutions to have an even greater impact on financial services industry," said Greg Shenkman, managing partner of Exigen Capital. "Mr. Bonati brings strong knowledge of the intersection of finance and technology, developed over more than two decades of experience. We are pleased to have his valuable counsel on the board of our European advisory firm."
Previously Bonati served as an executive board member of Credit Suisse Financial Services and division head of its Technology and Operations division where he was responsible for IT strategy, architecture, application development, IT infrastructure and datacenters, security and banking operations for its Global Private Banking, Retail Banking and Insurance interests.
Currently, Bonati is a senior consultant for the Business Industry Architecture Network (BIAN) and serves on several boards of directors, including: Swisscom IT Services; Tieto Oyi, a Scandinavian IT company listed at the Stock Exchange of Helsinki and Stockholm; and Zürcher Schauspielhaus AG.
Bonati completed executive programs at Harvard University and the Swiss Banking School (now the Swiss Financial Institute). He holds a Lic. Oec. HSG (equivalent to a Masters Degree in Economics) from the University of St. Gallen.
"The transformational capabilities of Exigen Capital are highly relevant to today's financial services industry," said Bonati. "I look forward to assisting my new colleagues in turning the industry's challenge into an opportunity to create value for the shareholders of banks and insurance companies as well as for the fund."
Exigen Capital is a Private Equity firm that invests to transform necessary but non-differentiating business functions into independent, profitable businesses; as well as in the technology and execution capabilities required to effect transformation. Greg Shenkman is managing partner. Its offices are located in San Francisco, New York, and Zurich, Switzerland.
For more information about Exigen Capital, please visit http://www.exigencapital.com.


Spinach has a high nutritional value, especially when fresh, frozen, steamed, or quickly boiled. Spinach is a rich source of vitamin A, vitamin C, vitamin K, magnesium, manganese, iron and folate; and a good source of the B vitamins – riboflavin and vitamin B6, vitamin E, calcium, potassium, and dietary fiber.
In this easy recipe for breakfast or side dish, I melded the nutrient-rich, green leaf vegetable of spinach with egg – a source of significant amounts of protein and choline, to create a nutritious and tasty egg scramble.
Sauté sliced pieces of a generous handful of spinach leaves in adequate quantity of warm ghee (clarified butter) until it condenses into thick gravy like consistency, blend whisked eggs (2) with the spinach gravy, add a pinch of turmeric powder to the mix, and smidgen the mix with salt – according to preference of flavour; cook the mixture until it thickens.


On April 8, 1897, a small fire began in back of a hotel in Gay Street's business district and quickly spread through the expensive establishments that were the pride of the city. The fire claimed five lives and a million dollars' worth of property. Citizens criticized the city council for spending money on a new market house and Centennial celebrations rather than spending more on the newly-established professional fire department. Ordinances regulating fire escapes and shared walls found new support, as well.
See also: Have you ever heard of Knoxville's Million Dollar Fire?


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Helping people discover more about God, grow into a family of faith, and live out Christ's love in the world.
4. Outreaching Community and In-reaching Fellowship – We strive to build community (reach in – Acts 2:44-46) for the purpose of impacting our community (reach out – Ephesians 4:11-13).
9. Humor, Fun and Abundant Life – Jesus said, "I came that they may have life and have it abundantly." (John 10:10). We are looking forward to the eternal joys of paradise, even as we strive to live in that joy today.


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Evolution of Containerships
Source: All dimensions are in meters. LOA: Length overall. The loads displayed on deck represent maximal possible loads, which would involve a large share of empty containers. The loads are usually 1 to 3 containers less in height. Containerships usually carry fewer containers because of weight restrictions and lack of demand on certain routes.
The definition of a containership class is a function of draft and related capacity in TEU. Since the beginning of containerization in the mid-1950s, containerships undertook six general waves of changes, each representing new generations of containerships:
A) Early containerships. The first generation of containerships was composed of modified bulk vessels or tankers that could transport up to 1,000 TEUs. The first containership, the "Ideal-X" was a converted World War II T2 tanker. At the beginning of the 1960s, the container was an untested transport technology and reconverting existing ships proved to be of lower costs and less risky. These ships were carrying onboard cranes since most port terminals were not equipped to handle containers. They were also relatively slow, with speeds of about 18 to 20 knots, and could only carry containers on the converted decks and not in their bellyhold. Once the container began to be massively adopted at the beginning of the 1970s, the construction of the first fully cellular containerships (FCC; second generation) entirely dedicated to handling containers started. The first cellular containerships, called the C7 class, were introduced in 1968. All containerships are composed of cells lodging containers in stacks of different heights depending on the ship's capacity. Cellular containership also offers the advantage of using the whole ship to stack containers, including below deck. Usually, an extra of two containers in width can be carried above deck than below deck. Cranes were removed from the ship design so that more containers could be carried (cranes remain today on some specialized containerships). The ability of ports to handle cellular containerships ceased to be a major concern with the setting of specialized container terminals worldwide. Cellular containerships were also much faster with speeds of 20-24 knots, which would become the speed of reference in containerized shipping.
B) Panamax. During the 1980s, economies of scale rapidly pushed for the construction of larger containerships. The larger the number of containers being carried the lower the costs per TEU. The process became a virtuous circle, compounding larger volumes and lower costs, which significantly helped the diffusion of the container and its use for international trade. The size limit of the Panama Canal, which came to be known as the Panamax standard, was achieved in 1985 with a capacity of about 4,000 TEUs. Once this limit was achieved, a decade passed before a new generation of larger containerships was designed. Simultaneously, Panamax container ship designs were evolving to take maximum advantage of the canal's limitation in beam. These ships came to be known as Panamax Max. The original dimensions of the Panama Canal, built by the US Army Corps of Engineers, are similar to the dimensions of the US Inland Waterways locks, resulting in narrow and long ship design.
C) Post Panamax I and II. Going beyond Panamax was perceived as a risk in terms of the configuration of shipping networks, additional handling infrastructure as well as draft limitations at ports. The APL C10 containership class, with a capacity of 4,500 TEUs, was introduced in 1988 and was the first containership class to exceed the 32.2 m width limit of the Panama Canal. By 1996, full-fledged Post Panamax containerships were introduced with capacities reaching 6,600 TEUs. The first Post-Panamax ship classes were not much longer than the Panamax class, but wider, making them more efficient. A ship above the Panamax size requires a substantial amount of cargo to be used profitably along a service loop and by the late 1990s, the rapid growth of global trade made such a ship class a marketable proposition. Once the Panamax threshold was breached, ship size quickly increased with capacities reaching 8,000 TEUs (Post Panamax II; "Sovereign Class"). Post Panamax containerships triggered an infrastructure challenge for many ports since they require deeper drafts (at least 43 feet) and highly efficient, but costly, portainers having wider reaches. Draft constraints became a factor placing pressures on ports to dredge to accommodate post-Panamax containerships.
D) New-Panamax, or Neo-Panamax (NPX). This refers to ships designed to fit exactly in the locks of the expanded Panama Canal, which opened in June 2016. These ships have a capacity of about 12,500 TEU, but there are several configurations of Neo-Panamax ships in terms of length (17 to 22 bays) and width (19 or 20 containers across). Like its Panamax counterparts, Neo-Panamax ships are likely to define a specific ship class able to service the Americas and the Caribbean, either from Europe or Asia. Neo-Panamax ships are likely to become the new standard in port infrastructure design for decades to come.
E) Very Large Containership (VLCS) and Ultra Large Containership (ULCV). By 2006, the third generation of post-Panamax containerships came online when Maersk shipping line introduced a ship class with a capacity in the range of 11,000 to 14,500 TEUs; the Emma Maersk (E Class). They were dubbed Very Large Containerships since they are bigger than the specifications of the expanded Panama Canal. A further extension of the post Panamax design led to the introduction of Ultra Large Containership class of 18,000 TEUs and above in 2013 (named 'Triple E' by Maersk). This class was further expanded, and by 2017, ships above 20,000 TEUs started to be delivered. An additional expansion in 2019 introduced ships of 24 containers across and with 24 bays, dubbed Megamax-24 (MGX-24). The ULCS/Megamax-24 is getting close to the technical limits that the Suez Canal can accommodate, beyond which the commercial relevance declines substantially. It remains to be seen which routes and ports these ships would service, but they are limited mostly to routes between Asia and Europe. There are larger ship designs on the drawing boards, such as the "Malacca Max" class that could carry about 27,000-30,000 TEU, but they are not expected to be constructed until there are sufficient volumes on the limited routes these ships could service.
Containership speeds have peaked to an average of 20 to 25 knots, and it is unlikely that speeds will increase due to energy consumption; many shipping lines are opting for slow steaming to cope with higher bunker fuel prices (when there are market spikes) and overcapacity (to have more ships in a slower service). The deployment of a class of fast containerships has remained on the drawing boards because the speed advantages they would confer would not compensate for the much higher shipping costs. Supply chains have been synchronized with container shipping speeds, and the setting of landbridges, such as the Eurasian landbridge, offers a competitive service for time-sensitive cargoes.
Each subsequent generation of containership faces a shrinking number of harbors able to handle them and place pressures on port infrastructure and equipment. Maritime shipping companies are incited to use the largest containerships possible on their shipping routes since they benefit from economies of scale. However, ports and inland transportation systems must provide substantial capital investment if they expect to accommodate larger containerships. Thus, operational limitations are to deploy ships bigger than 8,000 TEU in terms of ports of call and the required infrastructure to provide an acceptable loading and unloading throughput. Also, large containership deployments require a substantial amount of cargo to be commercially feasible, such as adequate service frequency. Containerships in the range of 5,500 to 6,500 TEU appear to be the most flexible in terms of the ports they can access and the market they can service since using larger ships require fewer port calls. Therefore, limits to economies of scale in container shipping are much more limited by commercial attributes than by technical constraints.
Contents › Chapter 5 – Transportation Modes › 5.4 – Maritime Transportation › Evolution of Containerships


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Home > Conferences > JOE > Vol. 51 > No. 3 (2013)
SpreaderCal: An MS Excel-Based Computer Program for Spreader Calibration
Roberto N. Barbosa, Louisiana State University Agriculture CenterFollow
A Microsoft Excel-based software was developed to help Extension professionals with spreader calibration. Small, turf-used ground spreaders as well as spinner-type spreaders used in row crop agriculture can be calibrated using this software. Spreaders used in aerial application of urea and seed rice can also be calibrated. The software calculates the most commonly sought information during a calibration such as: total swath, optimized swath according to coefficient of variation (CV), and application rate.
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.
Barbosa, R. N. (2013). SpreaderCal: An MS Excel-Based Computer Program for Spreader Calibration. The Journal of Extension, 51(3), Article 18. https://tigerprints.clemson.edu/joe/vol51/iss3/18
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One of the nice place in Tacloban City is The Family Park. This is good for friends / family outing, dating and even doing for exercise in the morning. A park located Magsaysay Avenue near Botanical Garden and UP Tacloban faces Cancabato Bay. No entrance fee needed be sure to have just a pocket money for snacks.
See the beauty of this place most especially in the morning to see the sunrise or in the west for the sunset waiting until the evening comes. After Family Park then next the best is to go Balyuan Amphitheater near Family Park to eat the barbecue. Barbecue vendors are friendly they speak politely to their customers.
Going this place picture taken must have to make more memorable outing.
Here are some pics from Family Park Tacloban City on Monday May 14, 2012.


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Nanalyze
Augmented/Virtual Reality
How Computational Chemistry Helps Drug Discovery
Steve Jobs once said that "the biggest innovations of the twenty-first century will be the intersection of biology and technology," and that "a new era is beginning" that he likened to the digital age that he spent most of his life working in. Numerous examples keep popping up that support his claims. You can now take someone's DNA, the recipe for their existence, and hand it over to them on a thumb drive in digital form. We've even created new DNA letters and expanded the genetic alphabet. We're using software programs to create synthetic organisms for the chemical industry that remove reliance on petroleum. We have the ability to edit genes at the germline and potentially remove hereditary diseases. And we're now able to start modeling how things actually work in nature. One exciting area of work where biology meets technology is computational chemistry.
Simply put, computational chemistry is a branch of chemistry that uses computer simulations to assist in solving chemical problems. It's not anything new, but the application of machine learning to computational chemistry is creating new opportunities. A paper on the topic describes this as follows:
Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry.
It's yet another application of deep learning that's emerging and now attracting lots of funding, particularly in the area of drug discovery. Computational chemistry can accelerate the long and costly drug discovery process which can be explained by the below diagram taken from a paper on the topic published in the Encyclopedia of Nanotechnology 2015.
Source: Paper on Computational Chemistry for Drug Discovery
The trillion-dollar pharmaceutical industry is currently facing a crisis of declining productivity, spending more on research each year, yet achieving fewer breakthroughs per dollar. Computational chemistry promises to decrease time-to-market and lower the overall cost of drug discovery. Consequently, venture capitalists have been pouring money into startups that are using computational chemistry for drug discovery. Let's take a look at some of them.
Some Computational Chemistry Companies
While most startups are private companies, not all private companies are startups. We usually consider startups to be recently established companies that have taken in venture capital funding. In the case of Schrödinger, they were established way back in 1990 and have slowly been taking in funding rounds totaling $137 million so far. Their most recent funding round – an $85 million Series E round – closed in January of this year with participation from Google Ventures and the Bill & Melinda Gates Foundation which led three previous investments in Schrödinger since 2010. The well-established firm is working with multiple partner companies to advance a diverse drug discovery pipeline. They also perform extensive research in-house and offer a suite of products for drug discovery and materials science.
The fact that they closed a single funding round this year that exceeded all their past funding rounds combined shows the need to scale their business in the face of increased competition from startups like this next one.
When naming your startup, it's always fun to take the piss out of your future marketing team by choosing a company name only a handful of people know how to pronounce. That's what XtalPi decided to do, and if you think their company name is cryptic, just wait until you read about their technology. Founded in 2014 by a group of quantum physicists at MIT, Boston startup XtalPi has taken in $67.5 million in funding to develop "state-of-the-art crystal structure prediction (CSP, also known as polymorph prediction) technology." A comprehensive paper on how crystal structures relate to drug discovery summarizes the application as follows:
Crystallization is one of the oldest separation and purification unit operations, and has recently contributed to significant improvements in producing higher-value products with specific properties and in building efficient manufacturing processes.
In other words, it's all about using machine learning and predictive analytics to improve the likelihood that a drug can make it through the drawn-out approval process. The majority of their funding came in the form of a Series B that closed last Fall which followed the announcement of a strategic research collaboration with Pfizer Inc. (NYSE:PFE) in May of last year. Investors in XtalPi include Google and a large Chinese representation including Renren, Tencent, and Sequoia Capital China. Google is also an investor in our next startup as well.
No technology should be considered exciting until Softbank comes by and dumps a truckload of money onto it. That's what happened to Relay Therapeutics late last year when they closed a $400 million Series C round led by Softbank which brought their total funding to a whopping $520 million. Founded in 2016 out of Cambridge, Massachusetts, Relay is initially focusing on developing therapeutics for oncology by using a computational chemistry approach which they describe as follows:
Many drugs are inhibitors that prevent the binding of a ligand to its active site. Relay Therapeutics' drug discovery engine is built on advances in detecting and characterizing dynamic interactions – anywhere on a protein – not just at the active site.
Put another way, Relay Therapeutics observes the motion of pharmaceutically relevant target proteins and then predicts how they will interact with hypothetical drug molecules. They believe that "putting protein motion at the heart of drug discovery" is a complete paradigm shift for the drug discovery process.
When trying to assess the merits of a startup in any given space, it's useful to look at how much funding they've raised, who they've raised the funding from, and who they've partnered with. In the case of this next startup, they've been securing partnerships left and right. Founded in 2012, Oxford UK startup Exscientia has raised $43.7 million in funding with more than half of that arriving this past January in the form of a $26 million Series B round. Participating in that round were two publicly traded pharma companies – Evotec and Celgene – both of whom are working with Exscientia. Just last month, Exscientia announced a three-year AI drug discovery partnership with Celgene which was said to be "the largest AI drug discovery deal done in the industry to date." Other names they're working with include Roche, Sanofi, and GlaxoSmithKline (GSK). Just days ago, Exscientia announced their first lead molecule to be delivered under their collaboration with GSK, targeting a key pathway for the treatment of Chronic Obstructive Pulmonary Disease (COPD).
Update 05/26/2020: Exscientia has raised $60 million in Series C funding to expand its portfolio and pipeline, bolster AI biology as part of its 'full-stack capabilities' as well as accelerate its international expansion, including building out its presence in the United States. This brings the company's total funding to $103.7 million to date.
Last but not least is a startup we took a look at a few years ago, Atomwise. In that article, we talked about the basic drug discovery process and how once there is a known protein, Atomwise can then figure out what molecule can bind to it and what will be safe. Total funding for Atomwise now sits at $51.3 million after they raised an additional $45 million in the form of a Series A round that closed in March of last year with participation that included Monsanto, Chinese firms Baidu and Tencent, and notable venture capital firms that included Khosla and DFJ. Atomwise claims their deep convolutional neural network, AtomNet, screens between 10 and 20 million compounds a day. If you want to know more, here's a paper that describes their technology (click the below image to access the entire paper).
A paper describing AtomNet – Source: Atomwise
Just this past January, AtomNet received a vote of confidence from Charles River (CRL), a $7 billion firm that offers a variety of pre-clinical and clinical laboratory services to the pharmaceutical industry. Both companies entered into a strategic relationship where Atomwise will "support hit discovery, hit-to-lead, and lead optimization efforts; while receiving technology access fees, milestone-based payments and royalties from clients," a number that could exceed $2.4 billion according to the press release. Clients can expect to see "an exponential reduction in the cost and risk of drug discovery."
Update 08/11/2020: Atomwise has raised $123 million to expand and build its own pipeline of experimental drugs. This brings the company's total funding to $174.3 million to date.
It's important to distinguish between "computational drug discovery" and "computational chemistry." There are now upwards of 120 startups out there working on various aspects of "computational drug discovery." That is, they're using new technologies like machine learning to improve the entire drug discovery process. "Computational chemistry" also falls under this label, but specifically refers to the use of computational methods to optimize a molecule once a target has been identified. All of the startups we've talked about have taken in sizable funding rounds in the last year and four of them have raised in the last six months. An industry expert we spoke to speculated that this rush of capital to computational chemistry startups is because they're optimizing a step in the drug discovery process that's easy to understand. In our next article on this topic, we'll take a look at some of the progress being made in the broader "computational drug discovery" space.
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