A traumatic brain injury (TBI) is a catastrophic injury that disrupts normal brain function and is caused by an outside force, rather than an internal problem like a stroke. Unlike most other tissues of the body, brain tissue does not grow back, which means that any tissue damaged or destroyed by the injury is gone. Because the brain controls everything we do -- physical movement, emotion, senses, personality and thinking -- this can have a devastating effect on victims. Charles Scholle is a preeminent Duluth brain injury attorney He leads our team of excellent lawyers and legal professionals. With our deep knowledge and expertise we navigate the challenging medical and legal issues involved with a TBI or traumatic head injury of any kind.
The most common brain injuries are concussions, but these can range from mild to serious and even a mild concussion can be very disruptive. As brain injury lawyers in Duluth, we know that brain injuries are far more common than most people realize, with about 1.4 million Americans sustaining a brain injury each year. Over five million Americans need lifelong or long-term help with daily life due to a brain injury.
Consult With Scholle Law, Duluth Brain Injury Lawyers as Soon as Possible.
If you, or someone you love, may have suffered a brain injury it is important to consult with an expert as soon as possible. This cannot be overemphasized. Sometimes after an accident, victims decline treatment because they do not realize they have sustained a brain injury. In addition, these injuries are not always easy to diagnose because they do not always leave a visible wound. Many brain injury and symptoms can take a few days or longer to become evident, which is why many brain injury victims decline treatment after an accident.
As brain injury lawyers in Duluth, we have fought against many insurance companies who view a delayed diagnosis as a sign that you are being dishonest about your injuries. These companies might try to avoid coverage. Even when your doctor diagnoses a brain injury, insurers may use a delay in treatment as an excuse to deny coverage for an expensive-to-treat brain injury. That is why the legal team at Scholle Law advise clients with a potential head injury to seek medical and legal advice, even if they feel fine.
Scholle law provides case evaluations for traumatic brain injuries call us toll-free at 866-972-5287 or 770-717-5100.
For over two decades, Duluth injury attorney Charles Scholle and the Scholle Law team have specialized in representing clients injured through the negligence of others. We bring significant expertise in cases involving very serious injury, including brain injury claims. Serious brain injury cases are legally and medically complex. Scholle Law understands not only the science involved, but how to value claims for damages in these cases, such as a lifetime of long-term care and a lost career. We have a record of positive results in these cases.
Perhaps most importantly, we understand what accident victims experience and work to ease their burdens by communicating closely and explaining things clearly and thoroughly, freeing them to get back to their jobs, their families and their lives. If you believe you have suffered a brain injury due to someone else's carelessness, contact Scholle Law for help.
To learn more about how we support our clients see: How a Personal Injury Case is Handled.


📬 envio correios ou retirada na Loja!!
#Cutterfly e il @salonefirriolo un abbinamento che ci piace tanto e che regalerà grandi soddisfazioni. Ne siamo certi!
E se você está procurando mais elegância, invista nessa combinação #atual e #versátil. Vista #koutage.
Можешь ли ты сказать "только это правильно"
Lost in the right direction.
So 90% of you said "yes please " to more Deluxe Dan Bobble Hats in my recent poll ( thank you - this really helps me) and of course I've listened!
You'll find a couple of newly listed cuties in my etsy shop ( link in bio) or you can send me a dm if you prefer .
Worldwide shipping always available and custom requests too.
☀ Pra amanhecer bem... sorria!!!!
Nova coleção @universodacrianca espera por você!
Mettete like e seguiteci per altri post.
Piese de servit, argint, Anglia, Harrison Brothers 1863, poanson regina Victoria, mânere din fildes, 383 grame.
In the age where everyone prefers partying , I choose learning new skills.


text:
using System.Collections.Generic;
using System.Linq;
using MailSim.Common.Contracts;
using System.IO;
using System.Dynamic;

namespace MailSim.ProvidersREST
{
    class MailItemProviderHTTP : HTTP.BaseProviderHttp, IMailItem
    {
        private Message _message;

        public MailItemProviderHTTP(Message msg)
        {
            _message = msg;
        }

        public string Subject
        {
            get
            {
                return _message.Subject;
            }

            set
            {
                _message.Subject = value;
            }
        }

        public string Body
        {
            get
            {
                return _message.Body.Content;
            }

            set
            {
                _message.Body = new ItemBody
                {
                    Content = value,
                    ContentType = "HTML"
                };
            }
        }

        public string SenderName
        {
            get
            {
                return _message.Sender.EmailAddress.Address;
            }
        }

        public void AddRecipient(string recipient)
        {
            _message.ToRecipients.Add(new Recipient
            {
                EmailAddress = new EmailAddress
                {
                    Address = recipient
                }
            });
        }

        public void AddAttachment(string filepath)
        {
            using (var reader = new StreamReader(filepath))
            {
                var contents = reader.ReadToEnd();

                var bytes = System.Text.Encoding.UTF8.GetBytes(contents);
                var name = filepath.Split('\\').Last();

                var fileAttachment = new FileAttachment
                {
                    ContentBytes = bytes,
                    Name = name,
                };

                HttpUtilSync.PostItem(Uri + "/attachments", fileAttachment);
            }
        }

        // TODO: Figure out how to implement this
        public void AddAttachment(IMailItem mailItem)
        {
#if false
            var itemProvider = mailItem as MailItemProvider;

            var itemAttachment = new ItemAttachment
            {
                Item = new Item { Id = itemProvider.Handle.Id },
                Name = "Item Attachment!!!",
            };

           Util.PostAsync<ItemAttachment>(Uri + "/attachments", itemAttachment).Wait();
#endif
        }

        // Create a reply message
        public IMailItem Reply(bool replyAll)
        {
            string uri = Uri + (replyAll ? "/CreateReplyAll" : "/CreateReply");

            Message replyMsg = HttpUtilSync.PostItem<Message>(Uri + "/CreateReply");

            return new MailItemProviderHTTP(replyMsg);
        }

        public IMailItem Forward()
        {
            Message msg = HttpUtilSync.PostItem<Message>(Uri + "/CreateForward");

            return new MailItemProviderHTTP(msg);
        }

        public void Send()
        {
            HttpUtilSync.PatchItem(Uri, _message);
            HttpUtilSync.PostItem<Message>(Uri + "/Send");
        }

       public void Move(IMailFolder newFolder)
        {
            var folderProvider = newFolder as MailFolderProviderHTTP;

            var folderId = folderProvider.Handle;

            dynamic destination = new ExpandoObject();
            destination.DestinationId = folderId;

            HttpUtilSync.PostItemDynamic<Message>(Uri + "/Move", destination);
        }

        public void Delete()
        {
            HttpUtilSync.DeleteItem(Uri);
            _message = null;
        }

        public bool ValidateRecipients()
        {
            // TODO: Implement this
            return true;
        }

        #if false
        internal Message Handle
        {
            get
            {
                return _message;
            }
        }
#endif
        private string Uri
        {
            get
            {
                return string.Format("Messages/{0}", _message.Id);
            }
        }

        internal class Message
        {
            public string Id { get; set; }
            public string Subject { get; set; }
            public ItemBody Body { get; set; }
            public ICollection<Recipient> ToRecipients { get; set; }
            public Sender Sender { get; set; }
            public string Importance { get; set; }
        }

        internal class ItemBody
        {
            public string Content { get; set; }
            public string ContentType { get; set; }
        }

        internal class Recipient
        {
            public EmailAddress EmailAddress { get; set; }
        }

        internal class EmailAddress
        {
            public string Address { get; set; }
            public string Name { get; set; }
        }

        internal class Sender
        {
            public EmailAddress EmailAddress { get; set; }
        }

        internal class FileAttachment
        {
            [Newtonsoft.Json.JsonProperty("@odata.type")]
            public string Type
            {
                get
                {
                    return "#Microsoft.OutlookServices.FileAttachment";
                }
            }

            public byte[] ContentBytes { get; set; }
            public string Name { get; set; }
        }

        internal class ItemAttachment
        {
            [Newtonsoft.Json.JsonProperty("@odata.type")]
            public string Type
            {
                get
                {
                    return "#Microsoft.OutlookServices.ItemAttachment";
                }
            }
            public string Name { get; set; }
            public Item Item { get; set; }
        }

        internal class Item
        {
            public string Id { get; set; }
        }
    }
}

redpajama_set_name: RedPajamaGithub


text:
La iglesia de San Lorenzo es un templo católico de estilo románico, pero con ampliaciones y acabamientos góticos, ubicado en la ciudad catalana de Lérida.
Es la segunda en importancia de la ciudad después de La Seu Vella.

Tiene tres naves de la misma altura, con tres ábsides. La nave central, de estilo románico, es la más antigua. Las otras dos naves son de estilo gótico. La construcción se inició a finales del siglo XII mediante los escultores y artesanos que trabajaban con Pere de Coma, maestro de La Seu Vella. Las capillas laterales y el campanario octogonal - del siglo XV - son de estilo gótico. El edificio conserva cuatro importantes retablo góticos, el más grande dedicado a San Lorenzo, el de Santa Ursula (atribuido a Jaume Cascalls), el de San Pedro y el de Santa Lucía. A la puerta gótica de la plaza de San Jose son visibles los blasones de Berenguer de Gallart. La iglesia, sede catedralicia en dos ocasiones, contiene diferentes piezas de arte pertenecientes al Museo Diocesano de Lérida.

Se destaca la escultura de la Madre de Dios de los Apóstoles o Verge dels Fillols del escultor Bartomeu de Robió, procedente de La Seu Vella y un cuadro gótico del siglo XV, Santa María de la Candelera, atribuido al pintor Mateu Ferrer y que representa a San Blas. En el año 2002 se llevó a cabo la restauración del campanario que ha recuperado su belleza gracias a la reproducción artesanal de los calaitos de la barandilla y de las gárgolas originales.

Véase también
 Anexo:Bienes de interés cultural de la provincia de Lérida

Enlaces externos

 San Lorenzo. La ciudad de Lérida

Lérida
Lérida
Bienes culturales de interés nacional de la provincia de Lérida
Lorenzo
Monumentos de Lérida
Monumentos histórico-artísticos de España declarados en 1931

redpajama_set_name: RedPajamaWikipedia


text:
Q: WiFi (r8712u) is only working after doing rmmod & modprobe My Wifi is not working after logging in. I have no clue why it starts working after manually doing a rmmod && modprobe.
I guess there is something wrong with the order during boot-up.
Below are two snippets of my dmesg. The first one is during normal boot-up.
The second one belongs to my manual rmmod/modprobe step.
Do you have any idea what I can do to fix this?
System: Kubuntu: 16.04 , 4.4.0-10-generic x86_64
[    7.087388] r8169 0000:07:00.0 enp7s0: link down
[    7.087428] IPv6: ADDRCONF(NETDEV_UP): enp7s0: link is not ready
[    7.089674] IPv6: ADDRCONF(NETDEV_UP): enp8s0: link is not ready
[    7.109848] r8712u: module is from the staging directory, the quality is unknown, you have been warned.
[    7.110464] r8712u: register rtl8712_netdev_ops to netdev_ops
[    7.110467] usb 1-4: r8712u: USB_SPEED_HIGH with 4 endpoints
[    7.110907] usb 1-4: r8712u: Boot from EFUSE: Autoload OK
[    7.282782] r8169 0000:08:00.0 enp8s0: link down
[    7.284825] IPv6: ADDRCONF(NETDEV_UP): enp8s0: link is not ready
[    7.617220] usb 1-4: r8712u: CustomerID = 0x000a
[    7.617222] usb 1-4: r8712u: MAC Address from efuse = 00:87:40:10:08:be
[    7.617223] usb 1-4: r8712u: Loading firmware from "rtlwifi/rtl8712u.bin"
[    7.617261] usbcore: registered new interface driver r8712u
[    7.621546] r8712u 1-4:1.0 wlx0087401008be: renamed from wlan0
[    7.732358] cfg80211: World regulatory domain updated:
[    7.732360] cfg80211:  DFS Master region: unset
[    7.732361] cfg80211:   (start_freq - end_freq @ bandwidth), (max_antenna_gain, max_eirp), (dfs_cac_time)
[    7.732363] cfg80211:   (2402000 KHz - 2472000 KHz @ 40000 KHz), (N/A, 2000 mBm), (N/A)
[    7.732365] cfg80211:   (2457000 KHz - 2482000 KHz @ 40000 KHz), (N/A, 2000 mBm), (N/A)
[    7.732366] cfg80211:   (2474000 KHz - 2494000 KHz @ 20000 KHz), (N/A, 2000 mBm), (N/A)
[    7.732368] cfg80211:   (5170000 KHz - 5250000 KHz @ 80000 KHz, 160000 KHz AUTO), (N/A, 2000 mBm), (N/A)
[    7.732370] cfg80211:   (5250000 KHz - 5330000 KHz @ 80000 KHz, 160000 KHz AUTO), (N/A, 2000 mBm), (0 s)
[    7.732371] cfg80211:   (5490000 KHz - 5730000 KHz @ 160000 KHz), (N/A, 2000 mBm), (0 s)
[    7.732373] cfg80211:   (5735000 KHz - 5835000 KHz @ 80000 KHz), (N/A, 2000 mBm), (N/A)
[    7.732374] cfg80211:   (57240000 KHz - 63720000 KHz @ 2160000 KHz), (N/A, 0 mBm), (N/A)
[    7.777931] nvidia-modeset: Allocated GPU:0 (GPU-129106a8-8953-c390-c543-a6e522aa3b5e) @ PCI:0000:03:00.0

After doing: sudo rmmod r8712u && sudo modprobe r8712u
[  188.145386] usbcore: deregistering interface driver r8712u
[  188.282787] usb 1-4: reset high-speed USB device number 2 using ehci-pci
[  192.478971] r8712u: module is from the staging directory, the quality is unknown, you have been warned.
[  192.481375] r8712u: register rtl8712_netdev_ops to netdev_ops
[  192.481380] usb 1-4: r8712u: USB_SPEED_HIGH with 4 endpoints
[  192.481947] usb 1-4: r8712u: Boot from EFUSE: Autoload OK
[  193.181882] usb 1-4: r8712u: CustomerID = 0x000a
[  193.181887] usb 1-4: r8712u: MAC Address from efuse = 00:87:40:10:08:be
[  193.181890] usb 1-4: r8712u: Loading firmware from "rtlwifi/rtl8712u.bin"
[  193.182264] usbcore: registered new interface driver r8712u
[  193.184687] r8712u 1-4:1.0 wlx0087401008be: renamed from wlan0
[  193.209228] IPv6: ADDRCONF(NETDEV_UP): wlx0087401008be: link is not ready
[  193.914988] r8712u 1-4:1.0 wlx0087401008be: 1 RCR=0x153f00e
[  193.915733] r8712u 1-4:1.0 wlx0087401008be: 2 RCR=0x553f00e
[  194.022709] IPv6: ADDRCONF(NETDEV_UP): wlx0087401008be: link is not ready
[  194.346729] IPv6: ADDRCONF(NETDEV_UP): wlx0087401008be: link is not ready
[  194.380906] IPv6: ADDRCONF(NETDEV_UP): wlx0087401008be: link is not ready
[  204.899115] IPv6: ADDRCONF(NETDEV_CHANGE): wlx0087401008be: link becomes ready

sudo lshw -class network
  *-network
       description: Wireless interface
       physical id: 1
       bus info: usb@1:4
       logical name: wlx0087401008be
       serial: 00:87:40:10:08:be
       capabilities: ethernet physical wireless
       configuration: broadcast=yes driver=r8712u ip=192.168.178.20 multicast=yes wireless=IEEE 802.11bgn

lsusb
Bus 001 Device 002: ID 0bda:8172 Realtek Semiconductor Corp. RTL8191SU 802.11n WLAN Adapter

Update: cat /var/log/syslog | grep NetworkManager
ar  5 21:28:12 ferhat-X58A-UD5 NetworkManager[882]: <info>  urfkill disappeared from the bus
Mar  5 21:28:12 ferhat-X58A-UD5 NetworkManager[882]: <info>  ofono is now available
Mar  5 21:28:12 ferhat-X58A-UD5 NetworkManager[882]: <warn>  failed to enumerate oFono devices: GDBus.Error:org.freedesktop.DBus.Error.ServiceUnknown: The name org.ofono was not provided by any .service files
Mar  5 21:28:12 ferhat-X58A-UD5 NetworkManager[882]: <info>  ModemManager available in the bus
Mar  5 21:28:12 ferhat-X58A-UD5 NetworkManager[882]: <info>  wpa_supplicant running
Mar  5 21:28:13 ferhat-X58A-UD5 NetworkManager[882]: <error> [1457209693.030273] [platform/wifi/wifi-utils-wext.c:519] wext_get_range(): (wlan0): couldn't get driver range information (19).
Mar  5 21:28:13 ferhat-X58A-UD5 NetworkManager[882]: <info>  (wlan0): driver WEXT range request failed
Mar  5 21:28:13 ferhat-X58A-UD5 NetworkManager[882]: <warn>  (wlan0): failed to initialize WiFi driver
Mar  5 21:28:13 ferhat-X58A-UD5 NetworkManager[882]: (NetworkManager:882): GLib-GObject-CRITICAL **: object NMDeviceWifi 0xfa83d0 finalized while still in-construction
Mar  5 21:28:13 ferhat-X58A-UD5 NetworkManager[882]: (NetworkManager:882): GLib-GObject-CRITICAL **: Custom constructor for class NMDeviceWifi returned NULL (which is invalid). Please use GInitable instead.
Mar  5 21:28:13 ferhat-X58A-UD5 NetworkManager[882]: <warn>  wlan0: factory failed to create device: (unknown)
Mar  5 21:28:13 ferhat-X58A-UD5 NetworkManager[882]: <info>  devices added (path: /sys/devices/pci0000:00/0000:00:1a.7/usb1/1-4/1-4:1.0/net/wlx0087401008be, iface: wlx0087401008be)
Mar  5 21:28:13 ferhat-X58A-UD5 NetworkManager[882]: <info>  device added (path: /sys/devices/pci0000:00/0000:00:1a.7/usb1/1-4/1-4:1.0/net/wlx0087401008be, iface: wlx0087401008be): no ifupdown configuration found.
Mar  5 21:28:17 ferhat-X58A-UD5 NetworkManager[882]: <info>  startup complete

A: You are running Ubuntu 16.04 which is not yet fully tested and debugged and therefore not fully ready for release. I believe the proposed version of Network Manager contains a bug. I am unsure if there is any way to install an earlier version of NM in 16.04. https://bugs.launchpad.net/ubuntu/+source/network-manager/+bug/1489154
In the meantime, I suggest you do:
gksudo gedit /etc/rc.local

Use nano or kate or leafpad if you don't have the text editor gedit. Change the file to read:
#!/bin/sh -e
#
# rc.local
#
# This script is executed at the end of each multiuser runlevel.
# Make sure that the script will "exit 0" on success or any other
# value on error.
#
# In order to enable or disable this script just change the execution
# bits.
#
# By default this script does nothing.

sleep 3
service network-manager restart

exit 0

Proofread carefully, save and close the text editor.
Reboot. Any improvement?

redpajama_set_name: RedPajamaStackExchange


text:
DMO to FG: Grow revenue sources, cut waste to reduce borrowing
by Busola Aro 0 Comments
The Debt Management Office (DMO) says the federal government can reduce its dependence on borrowings to finance budget deficits by improving its revenue drive and prioritising expenditure.
Patience Oniha, director-general, DMO, said this on Tuesday during a presentation at an executive course on budgeting and fiscal responsibility organised by the Fiscal Responsibility Commission in Abuja.
In her paper titled "debt sustainability challenges and strategic revenue mobilisation initiative", Oniha said the country had run deficit budgets for many decades, which made borrowings from local and external sources imperative.
She advised the government to grow revenue and cut waste.
"A budget may be surplus, balanced or deficit. Nigeria has run deficit budgets on a consecutive basis for decades," she said.
"The financing of the deficits through borrowing from local and external sources is the principal reason for the growth in debt stock and debt servicing.
"One way to reduce budget deficits is to grow revenues; the other way is to prioritise expenditure and cut waste and leakages."
According to her, the country's total debt stock includes federal and sub-national governments.
Oniha added that amid low revenue, the government was able to deliver on infrastructure development and other responsibilities through borrowings.
She, however, said borrowing is not peculiar to Nigeria as countries across the world are borrowing.
"Debt levels are rising everywhere. Global revenue dropped as a result of Covid-19, and the Russian-Ukraine conflict has also increased borrowings," she added.
"Our problem is debt service to revenue ratio."
The DMO director-general further said a larger percentage of the country's borrowings were concessionary loans from multilateral and bilateral sources.
She said Nigeria's debt to GDP ratio was 23 percent, which is still within the country's self-imposed limit of 40 percent.
She also said the figure was within the World Bank and International Monetary Fund recommended limit of 55 percent for countries within Nigeria's peer group and 70 percent for ECOWAS countries.
In June 2022, Nigeria's total public debt stood at N42.84 trillion ($103.31 billion).
Debt Management Office(DMO)International Monetary Fund (IMF)World Bank
Honouring the healer: Critical role of patients in strengthening relationship with doctors

redpajama_set_name: RedPajamaCommonCrawl


Due to lacking predictors of depression recovery, successful treatment of major depressive disorder (MDD) is frequently only achieved after therapeutic optimization leading to a prolonged suffering of patients. This study aimed to determine neural prognostic predictors identifying non-remitters prior or early after treatment initiation. Moreover, it intended to detect time-sensitive neural mediators indicating depression recovery. This longitudinal, interventional, single-arm, open-label, phase IV, pharmacological functional magnetic resonance imaging (fMRI) study comprised four scans at important stages prior (day 0) and after escitalopram treatment initiation (day 1, 28, and 56). Totally, 22 treatment-free MDD patients (age mean ± SD: 31.5 ± 7.7; females: 50%) suffering from a concurrent major depressive episode without any comorbid DSM-IV axis I diagnosis completed the study protocol. Primary outcome were neural prognostic predictors of depression recovery. Enhanced de-activation of anterior medial prefrontal cortex (amPFC, single neural mediator) indicated depression recovery correlating with MADRS score and working memory improvements. Strong dorsolateral PFC (dlPFC) activation and weak dlPFC-amPFC, dlPFC-posterior cingulate cortex (PCC), dlPFC-parietal lobe (PL) coupling (three prognostic predictors) hinted at depression recovery at day 0 and 1. Preresponse prediction of continuous (dlPFC-PL: R2day1 = 55.9%, 95% CI: 22.6–79%, P < 0.005) and dichotomous (specificity/sensitivity: SP/SNday1 = 0.91/0.82) recovery definitions remained significant after leave-one-out cross-validation. Identified prefrontal neural predictors might propel the future development of fMRI markers for clinical decision making, which could lead to increased response rates and adherence during acute phase treatment periods. Moreover, this study underscores the importance of the amPFC in depression recovery.
Major depressive disorder (MDD) is highly prevalent leading to increased disability and mortality1. About two-thirds of all patients suffer from residual symptoms after first-line treatment with selective serotonin reuptake inhibitors (SSRIs)2,3. As a consequence, treatment optimization is common clinical practice resulting in a prolongation of disability and suicidal ideation4,5.
The clinical necessity of depression recovery (DR) stratification6,7,8 has propelled research of clinical9, genetic10,11,12, and neural predictors13. Several studies highlighted the role of the anterior medial prefrontal cortex (amPFC), as well as the anterior (ACC) and posterior cingulate cortex (PCC) in the prediction of DR after SSRI treatment14,15,16. Support is also provided by studies applying different treatment modalities17,18,19 and by research underscoring the importance of these brain regions in MDD pathobiology20,21. Beyond the amPFC, regions such as the anterior insula13,22, or dorsolateral PFC (dlPFC)23,24 were further suggested as predictors of DR.
Unfortunately, even a large body of cross-sectional imaging studies has failed to conclusively identify brain mechanisms responsible for DR7,8,13,25,26. This lack of consistency is not surprising considering the temporal dynamics of interactions between mentioned brain systems13 affecting the individual outcome over the course of illness27,28. One promising approach assessing such longitudinal interactions is to study neural mediators and prognostic predictors of DR, which have hardly been investigated so far13,25 despite their obvious clinical implications6,8,13,29,30. In this context, a prognostic predictor is defined as a treatment- and recovery-preceding, cross-sectional imaging characteristic related to DR13,30,31. Hence, it is capable of identifying nonresponders prior any clinical sign of improvement. Consequently, it is uncorrelated to changing neuroimaging measures along recovery and thus time-invariant12,13. A neural mediator, however, exhibits typical changes that might reflect neural processes unfolding in concert with recovery from depression13,30,31. Hence, it is per definition a time-sensitive imaging measure correlating with DR12,13. Importantly, predictors based on longitudinal data differ substantially from a single predictor originating from a cross-sectional study design lacking any distinction between time-sensitive (mediator) and time-invariant (prognostic predictor) brain processes13,27,32,33. As a critical clinical consequence, validated prognostic predictors would provide objective markers of nonresponse that are available prior treatment initiation, whereas mediators support decisions of clinicians, researchers and drug developers along treatment13. Previous studies34,35,36 investigated unspecific "prognostic predictors"31 to foresee nonresponse across treatment groups. In contrast to "prescriptive predictors"31 of treatment-specific outcomes, these findings might improve our understanding of mechanisms involved in a suboptimal DR that are not targeted by current treatments to propel future developments.
The primary goal of this exploratory, longitudinal, pharmacological functional magnetic resonance imaging study (phMRI) was to determine neural prognostic predictors of DR. Moreover, we expected to gain insights into the temporal dynamics of DR by assessing neural mediators13,27,28,33 along treatment. Previous clinical37,38,39 and imaging studies40,41,42 provide compelling evidence that highly persistent cognitive symptoms such as memory deficits or rumination are related to unfavorable illness course in terms of onset, DR, chronicity and future relapse. However, a more direct cognitive measure is required for a clinical application41. The frequently used n-back working memory (WM) task43 is well suited to assess these underlying cognitive functions as the interaction between networks of cognitive control (e.g., the dlPFC/fronto-parietal control network)42,44 and emotional processing (e.g., the amPFC/default mode network, DMN)40,45. Particularly the n-back task-negative DMN46 has been less prone to artifacts as compared to other standard paradigms applied in MDD research47,48. Escitalopram was chosen as treatment, because it is the most-selective49 and most-prescribed SSRI worldwide2,3. During this 9 weeks lasting clinical trial, all 22 MDD patients underwent 4 scanning sessions resulting in a total of 88 functional acquisitions. Four scanning sessions were performed: at baseline (day 0, d0), after initial escitalopram treatment (day 1, d1), and twice in monthly intervals (day 28 and day 56), where a clinical response is expectable. First, we determined neural predictors of DR within an activation analysis. Next, we performed functional connectivity (FC) analyses for significant brain regions in order to investigate findings on a brain systems level. Finally, we assessed the impact of neural predictors of DR on cognitive performance and clinical parameters.
MDD outpatients were recruited at the outpatient clinic or by online and bulletin board advertisements. Patients were invited to the Department of Psychiatry and Psychotherapy at the Medical University of Vienna (MUV) to participate in this longitudinal, interventional, single-arm, open-label, phase IV phMRI study. Enrollment was under supervision of LP after a comprehensive clinical assessment including previous medical and psychiatric history, neurological, and medical examinations such as routine laboratory testing, electrocardiography, and blood pressure measurement. The following inclusion criteria were applied: (1) MDD diagnosis according to DSM-IV (German Structured Clinical Interview, SCID-I)50 and absence of any other axis I disorder, (2) Montgomery-Åsberg Depression Rating Scale (MADRS) score ≥20 and ≤30, (3) age between 18 and 50 years, (4) right-handedness, and (5) willingness to provide informed consent and ability to be managed as outpatient. Detailed exclusion criteria are listed in the supplement. Out of 26, 22 included patients completed the study protocol (Figure S1). Reasons for study dropout were: corrupted MRI data (n = 1), lacking adherence to the study protocol due to increased anxious distress (n = 2), and exclusion due to medical reasons unrelated to the study medication (n = 1). The study protocol was approved by the local Ethics Committee (1060/2010) according to the Declaration of Helsinki. Please note four deviations after trial registration: (1) less subjects enrolled due to lower drop-out rates, (2) inclusion of healthy controls (HC), (3) one scan (d28) added as we recognized the strength of a longitudinal design before enrollment, and (4) more importance attached to the MRI-outcome, because genetic effects are envisioned as small and below clinical importance10.
Patient recruitment, scanning and data analysis took place between 2011 and 2017. All patients underwent 4 MRI scanning sessions prior (d0), 4–8 h (d1), 4 (d28), and 8 weeks (d56) after escitalopram treatment initiation. Imaging data of gender- and age-matched HC were retrieved from a previously published cross-sectional study40 subserving as control group for untreated patients (d0). Escitalopram dosing reflected clinical practice with a fixed dose of 10 mg and the option to increase to 20 mg after d28 until the end of study visit in case of nonresponse (n = 9). The primary measure of DR was defined as percent change between pretreatment (d0) and end-of-treatment (d56) MADRS scores: DR = (1-MADRSd56/MADRSd0)*100. MADRS was utilized to calculate DR due to its superior sensitivity to symptom change and its dominant use in clinical trials investigating escitalopram51. Clinical variables and interviews including the Hamilton Rating Scale for Depression (HAMD-17) and Anxiety (HAMA), and the Clinical Global Impressions (CGI) scale were employed to evaluate clinical prognostic predictors of DR and to exclude confounding collinearity (Table 1; Tables S2 and S3).
Subjects performed the n-back task comprising two levels (0-back, 2-back) in each of the four longitudinal MRI sessions. Longitudinal WM performance defined40 as percent correct responses (2-back accuracy, %) was correlated with clinical and imaging data (Fig. 2b, Table S2). Data from a 3 T Siemens TRIO scanner (12-channel standard head coil, Siemens Healthcare Systems, Germany) was preprocessed with AFNI (http://afni.nimh.nih.gov/afni/) implemented into an R framework (http://cran.r-project.org/), as described previously40 and in the supplement.
Second-level analysis of longitudinal activation data utilized a linear-mixed effects model (3dLME) that included first-order autocorrelations between consecutive sessions52,53. To find time-invariant prognostic predictors of DR, we calculated the DR main effect on neural activation across scan sessions13,30,31. To detect time-sensitive neural activation that could mediate DR, the interaction-term of DR and scan session was calculated13,30,31. All computed models further included age, gender, and scan session as nuisance variables. Random effects were defined for intercept and slope across scan sessions to improve generalizability54,55.
Second-level analyses were performed on seed-to-voxel FC maps in analogy to our activation analysis by using 3dLME52,53. Context-independent FC analysis correlated time-series after removing task-evoked co-activations40,47. Psycho-physiological interaction (PPI) analyses mapped the integration of seed regions specifically during 0B and 2B conditions, respectively. Activation analyses identified two significant clusters that defined our seed regions of interest (ROIs: amPFC mediator/dlPFC prognostic predictor of DR; red/purple, Fig. 1a).
Fig. 1: Neural mediator and prognostic predictors of depression recovery.
Correlations between 2B accuracy, MADRS scores and all four imaging clusters were calculated (Fig. 2). DR outcome was primarily defined continuous to avoid power loss entailed by artificial dichotomization56. Still, clinical decision-making benefits from the prediction of dichotomized endpoints (MADRSd56). Hence, post hoc receiver operating characteristics (ROC, Fig. 3, Figure S5, Table S5) illustrate the prediction of a dichotomous endpoint defined as the median-split corresponding to MADRSd56 values ≤5 for remitters and ≥10 for nonremitters and complying with clinical cut-offs for remission57. Leave-one-out cross-validation (CV) was applied to improve generalizability, comparability and to avoid overfitting25 (Table 2, Table S5). Statistics were prepared in R 3.1.2 (http://cran-r-project.org/) on extracted means of significant clusters (P < 0.05 corrected, two-tailed). All models combining several predictors (Fig. 3b and Figures S4 and S5) were based on a uniformly weighted sum of values that were scaled and centered before. This additive score is envisioned as more intuitive and robust than using optimized weights for each predictor that likely entail overfitting58.
Fig. 2: Working memory performance and mediator of depression recovery.
Fig. 3: Diagnostic ability of imaging predictors to anticipate depression recovery: leave-one-out cross-validation (solid line, CV, n − 1 patients) vs. full sample effect size estimations (dashed line, n patients).
A total of 22 adult MDD patients (22–46 years; mean ± SD = 31.5 ± 7.7; 50% females) with a concurrent major depressive episode completed the study protocol. A total of 66 HC were exactly matched for patients' gender (50% females), but were significantly younger also due to the relatively large number of HC (22–43 years; mean ± SD = 26.3 ± 3.4; t(24) = 3.1, P = 0.005). Patients (Table 1) were unmedicated for at least two months at d0 and suffered predominantly from a moderate MDE (27% mild, 55% moderate, 18% severe)59. A large proportion of patients was antidepressant naive (41%) and had never received previous psychopharmacological (50%) or psychotherapeutic treatment (55%). Clinical predictors revealed a trend towards better DR for patients with no previous antidepressant treatment (Table 1, left). Remaining baseline characteristics including WM performance did not predict later DR.
The recruitment of neural networks during task performance was comparable to previous reports40 (Supplement, Figure S2, Table S1). Neural mediators of DR were defined13,30,31 as time-sensitive interaction effects of DR on brain activation for all scan sessions (d0, d1, d28, and d56). One cluster comprising the amPFC reached statistical significance (orange, Fig. 1, Table 2). After clinical response, enhanced de-activation in this region was predictive for later DR measured from baseline to end-of-study (Fig. 1). Moreover, amPFC de-activation was related to depression severity (Fig. 2a, Table S2) and WM performance improvements (Fig. 2b, pd28,d56 < 0.05). Average WM performance improved along with depression symptom remission mainly between sessions d1 and d28 and to a level comparable to HC (Fig. 2a, Table S2). This cannot simply be explained by training effects, which are expected to be maximally between d0 and d1 due to novelty effects. Activation changed but remained within the range observed in HC (Fig. 1, density plot).
Neural prognostic predictors of DR were defined13,30,31 as the time-invariant main effect of DR for each scan session. Our analysis revealed one significant activation cluster encompassing the right dlPFC and parts of the adjacent anterior insula (green, Fig. 1, Table 2). Both preresponse sessions (d0, 1) predicted a beneficial DR in case of strong dlPFC predictor activation, as confirmed by conservative CV (Table 1, Table S3). Next, the amPFC and dlPFC clusters resulting from activation analyses were used as seeds for context-independent and context-dependent (PPI) FC analyses. Context-independent FC of the dlPFC seed revealed two significant clusters within the DMN: PCC and amPFC (lightblue/blue, Fig. 1, Table 2). Context-dependent FC analysis showed an interaction of dlPFC-PL integration and 0B activation in the parietal lobe (PL, purple, Fig. 1, Table 2). Hence, responders showed weaker dlPFC-PL integration during the 0B conditions and/or their dlPFC suppresses PL 0B activation. Across all session and predictors, a weaker FC of the dlPFC was found beneficial for DR while all values were within the range of HC (density plot, Fig. 1).
We noticed that the dlPFC seed revealed a predictive cluster in the amPFC (blue, Fig. 1a), but we found no cluster in the dlPFC or elsewhere after using the partly overlapping anterior-perigenual amPFC seed (orange, Fig. 1a) despite the undirected nature of FC analyses. Hence, we post hoc analyzed Harvard-Oxford atlas-defined60,61 masks (Figure S7) and spherical seeds62 (supplemental video). The atlas-defined subgenual seed revealed the largest prognostic predictor cluster in the dlPFC of all three averaged ACC/amPFC masks (see Figure S7). Still, the punctum maximum was located neither in the mediator region nor in the posterior subgenual ACC (see sACC and pACC labels in the video), but spread from perigenual (i6) to anterior subgenual ACC regions (i8).
The clinical significance and large effects63 of scans conducted prior to clinical response (d0, 1) as predictors of DR are highlighted by the conservative CV (Fig. 3a, Table 1, Table S3). Longitudinally, prognostic predictors tend to show the largest effects ahead of response (max. CV R2: dlPFC-PL FCdd1 = 55.8%). Combined with other consistency measures (Figure S3 CD; Table S4), this indicates that timing matters, and, statistically speaking, a low between-session interchangeability64. Incorporating multiple brain regions (Fig. 3b) and scans (Figure S4) improved minimal rather than maximal predictive performance, thus enhancing prognostic stability. Inclusion of motion nuisance and clinical variables did not alter these results (Table S3). As expected, model overfitting occurred predominantly for multivariate models after incorporating multiple brain regions according to differences between conservative CV and standard full sample results (solid vs. dashed lines, Fig. 3b).
Prediction of the dichotomous remission outcome (ROC after CV, Fig. 2, Figure S5) showed large effects for all univariate FC prognostic predictors at baseline (Table S5, area under the curve, AUCd0,d1,Σ(d0,1) > 0.79). The dlPFC-PL prognostic predictor provides an optimal specificity and sensitivity trade-off (Youden index = SP + SN-1: SP/SNd0 = 1/0.73; SP/SNd1 0.91/0.82) close to the clinically important specificity-optimized (SPmax) cut-off, which maximizes the detection of nonremitters.
This study aimed to identify neural prognostic predictors to anticipate suboptimal DR at four important treatment stages prior and during antidepressant treatment. Moreover, we analyzed changes of neural mediators, which are thought to trace brain systems functionally and causally related to the later clinical outcome. Finally, we evaluated these markers in terms of clinical use by behavioral data analysis and conservative effect size measures.
Consistent with cross-sectional imaging studies14,15,40, we identified potential neural markers of DR. Enhanced amPFC de-activation (mediator) correlated with symptom alleviation and therefore DR. In contrast, stronger dlPFC activation accompanied by a weaker coupling between dlPFC-amPFC, dlPFC-PCC, and dlPFC-PL (prognostic predictors) indicated beneficial DR23,24,40. These prognostic predictors showed statistically and clinically significant effects predominantly prior to clinical response. The supplemental video shows the spatial distribution of all effects located in the ACC/amPFC.
The detected neural mediator suggests that changes of amPFC de-activation are crucial to mitigate depressive symptoms40,45,65 (Fig. 1) and, specifically, persistent residual cognitive impairments41,66 (Fig. 2b) after clinical response (d28, d56). Hence, the amPFC mediator might inform clinical decision-making at early stages of therapy13,30 underlining the critical role of the DMN in DR13,40,45. Changes in amPFC activation, a region with relatively high serotonin transporter density67, were also observed in human SSRI-challenge studies46,68 and correlated to serotonin reuptake velocity in platelets69. Previous longitudinal SSRI treatment studies described functional changes within the amPFC in MDD responders14,19,70,71. Interestingly in terms of MDD treatment-specificity, this brain region was not only related to recovery in studies investigating serotonergic compounds, but also other antidepressants17, placebo effects72, psychotherapy19, deep brain stimulation18, and sleep deprivation73. On a clinical level, the amPFC was previously associated with rumination by using the same n-back experiment40. This corroborates the notion that decreasing amPFC activation during externally oriented tasks (e.g., n-back) represents successful DMN suppression necessary for cognitive performance and style improvement40,45,65,74 crucial for DR37.
Contrasting the mediator, the ascertained prognostic predictors of DR were statistically significant ahead of initial clinical response (Fig. 1). Stronger dlPFC activation and a weaker context-independent (dlPFC-amPFC, dlPFC-PCC) and context-dependent coupling during 0B conditions (dlPFC-PL) showed favorable effects on DR across all four sessions. The "cognitive neuropsychological" model42 of depression and a rich body of imaging studies46,69 indicate that SSRIs target primarily medial rather than lateral regions in the PFC46,75. In line with this model, we observed dynamic changes in the medial PFC and persistent factors in the lateral PFC, although both were markers for DR. These regions putatively cooperate like a pilot (amPFC/mediator) communicating with the air traffic control tower (dlPFC activation and FC/prognostic predictors). If you treat pilots to help them reaching their target, you also need a sanity check of the interplay with the control tower. The brain system orchestrated by the dlPFC might form such an auxiliary top-down regulation system. The n-back task tests the capacity of this system by increasing the limbic bottom-up interference during less-demanding 0B conditions42 in analogy to challenging weather conditions in a flight simulator. This might explain the additional prognostic predictor during 0B conditions (dlPFC-PL FCd, Fig. 1). The pattern of brain regions involved in this system (dlPFC, PL, dorsal amPFC) can be associated with reappraisal, selective attention and distraction76. These are emotion regulation strategies that require patients to (A) actively control (B) a model-based change while (C) explicitly holding mental representations of their goal within their mind77,78. These cognitive capacities tested by n-back79 and other prefrontal batteries were clinically related to a suboptimal course of depression80,81 and might be important targets of complementary treatments26,42,82,83,84.
This study highlights advantages of longitudinal phMRI prediction studies13,27,52. However, statistical significance is not sufficient to inform clinical decision-making8,29,85. As desired in prediction, pre-response imaging markers (Figure S4 R2d0,d1,Σ(d0,1)) tend to show larger effects as compared to post-response imaging or also preresponse clinical9 markers. Prognostic predictors are conceptualized as time-invariant and uncorrelated to DR itself12,13,29. Nonetheless, relatively large multisession effects (Figure S4) and low between-session consistency (Figure S3AB, Table S4) suggest that all scans contribute unique information from distinct treatment phases with low interchangeability64. ROC analyses (Fig. 3) facilitate translation of continuous DR predictors to predictors of dichotomous remission with known statistical costs to support decision-making as desired by most clinicians56. These results (Fig. 3a) suggest that an automatized measure (dlPFC-PL FCd) obtained by a single non-invasive 15-min fMRI is capable to improve patient stratification86. The threshold of clinically acceptable misclassifications might be larger in this context compared to, e.g., screening of orphan diseases, because it critically depends on two factors. Firstly, low economic and safety risks of (falsely) initiated second-line treatments would favor ordering the test, which would remain to positively influence clinical decision making8. Secondly, the pretest probability is favorably high due to a moderate antidepressant treatment response rate of 50–60%5,87.
This study has several limitations. Considering that secondary analyses are nonindependent from voxel-wise analyses, effect sizes likely remain inflated even after CV due to circularity88. Clinical prediction studies typically include CV, which is the necessary tough not sufficient step before clinical translation. This step is rarely applied in imaging prediction studies25,89 although the out-of-sample model could dramatically change results, as shown in our study (e.g., Figure 3). Still, our results primarily apply to noncomorbid, moderately depressed, nonsuicidal, and rather young adult patients without any history of previous treatment-resistance. Hence, results in comorbid, adolescent, geriatric, suicidal, therapy-resistant or -refractory MDD samples might differ substantially90. Since the intention of this study was to investigate prognostic predictors and mediators in a real-world clinical scenario with higher external validity and not the efficacy of the most prescribed antidepressant2,3,75, we employed an open-label study design without any placebo control, randomization, or blinding. Given the existence of intermixed placebo and drug effects in daily clinical practice8, neural predictors and mediators are explicitly categorized as nonspecific prognostic and not prescriptive predictors of DR29,31,87. However, we feel confident that placebo effects did not severely confound our interpretation, since we focused primarily on the clinically more important group of nonresponders, in which no placebo response is expected given the absence of any relevant overall treatment effect. Further, in contrast to between-group designs (MDD vs. HC, Fig. 1), within-group designs, and, particularly, predictive studies intend to utilize heterogeneity in neural patterns to reliably stratify patients without necessarily understanding the underlying nosology and etiology6,7,25.
Summarizing, our phMRI study characterized the longitudinal dynamic of neural prognostic predictors and a mediator candidate of DR. Enhanced de-activation of the amPFC (mediator) in remitters underscores the importance of the DMN in DR. Weaker activation and stronger FC of the dlPFC (prognostic predictors) was related to insufficient DR, which highlights the possibility to identify MDD non-remitters prior to treatment initiation. If replicated, these data encourage the clinical use of fMRI for individual risk prediction of a suboptimal illness course, which is urgently needed given the present insufficient sequential treatment algorithms.
Vos, T. et al. Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380, 2163–2196 (2012).
Bauer, M., Severus, E., Moller, H. J. & Young, A. H. Disorders WTFoUD. Pharmacological treatment of unipolar depressive disorders: summary of WFSBP guidelines. Int J. Psychiatry Clin. Pract. 21, 166–176 (2017).
Cleare, A. et al. Evidence-based guidelines for treating depressive disorders with antidepressants: a revision of the 2008 British Association for Psychopharmacology guidelines. J. Psychopharmacol. 29, 459–525 (2015).
Gaynes, B. N. et al. Treating depression after initial treatment failure: directly comparing switch and augmenting strategies in STAR*D. J. Clin. Psychopharmacol. 32, 114–119 (2012).
Rush, A. J. et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am. J. Psychiatry 163, 1905–1917 (2006).
Hahn, T., Nierenberg, A. A. & Whitfield-Gabrieli, S. Predictive analytics in mental health: applications, guidelines, challenges and perspectives. Mol. Psychiatry 22, 37–43 (2017).
Perlis, R. H. Translating biomarkers to clinical practice. Mol. Psychiatry 16, 1076–1087 (2011).
Iniesta, R. et al. Combining clinical variables to optimize prediction of antidepressant treatment outcomes. J. Psychiatr. Res. 78, 94–102 (2016).
Rabl U., Meyer B. M., Pezawas L. Variability of antidepressant drug response: contribution of imaging genetics studies. in (eds Bigos, K. L., Hariri, A. R., Weinberger, D. R.). Neuroimaging Genetics: Principles and Practices (Oxford; New York: Oxford University Press, 2016).
Kato, M. & Serretti, A. Review and meta-analysis of antidepressant pharmacogenetic findings in major depressive disorder. Mol. Psychiatry 15, 473–500 (2010).
Cattaneo, A. et al. Candidate genes expression profile associated with antidepressants response in the GENDEP study: differentiating between baseline "predictors" and longitudinal "targets". Neuropsychopharmacology 38, 377–385 (2013).
Phillips, M. L. et al. Identifying predictors, moderators, and mediators of antidepressant response in major depressive disorder: neuroimaging approaches. Am. J. Psychiatry 172, 124–138 (2015).
Kraus C., et al. The pulvinar nucleus and antidepressant treatment: dynamic modeling of antidepressant response and remission with ultra-high field functional MRI. Mol. Psychiatry. 1476-5578 (2018). Epub ahead of print.
Pizzagalli, D. A. Frontocingulate dysfunction in depression: toward biomarkers of treatment response. Neuropsychopharmacology 36, 183–206 (2011).
Spies, M. et al. Default mode network deactivation during emotion processing predicts early antidepressant response. Transl. Psychiatry 7, e1008 (2017).
Salvadore, G. et al. Anterior cingulate desynchronization and functional connectivity with the amygdala during a working memory task predict rapid antidepressant response to ketamine. Neuropsychopharmacology 35, 1415–1422 (2010).
Holtzheimer, P. E. et al. Subcallosal cingulate deep brain stimulation for treatment-resistant unipolar and bipolar depression. Arch. Gen. Psychiatry 69, 150–158 (2012).
Goldapple, K. et al. Modulation of cortical-limbic pathways in major depression: treatment-specific effects of cognitive behavior therapy. Arch. Gen. Psychiatry 61, 34–41 (2004).
Pezawas, L. et al. 5-HTTLPR polymorphism impacts human cingulate-amygdala interactions: a genetic susceptibility mechanism for depression. Nat. Neurosci. 8, 828–834 (2005).
Drevets, W. C. et al. Subgenual prefrontal cortex abnormalities in mood disorders. Nature 386, 824–827 (1997).
McGrath, C. L. et al. Toward a neuroimaging treatment selection biomarker for major depressive disorder. JAMA Psychiatry 70, 821–829 (2013).
Gyurak, A. et al. Frontoparietal activation during response inhibition predicts remission to antidepressants in patients with major depression. Biol. Psychiatry 79, 274–281 (2016).
Langenecker, S. A. et al. Frontal and limbic activation during inhibitory control predicts treatment response in major depressive disorder. Biol. Psychiatry 62, 1272–1280 (2007).
Gabrieli, J. D., Ghosh, S. S. & Whitfield-Gabrieli, S. Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron 85, 11–26 (2015).
Derubeis, R. J., Siegle, G. J. & Hollon, S. D. Cognitive therapy versus medication for depression: Treatment outcomes and neural mechanisms. Nat. Rev. Neurosci 9, 788–796 (2008).
Nelson, B., McGorry, P. D., Wichers, M., Wigman, J. T. W. & Hartmann, J. A. Moving from static to dynamic models of the onset of mental disorder: a review. JAMA Psychiatry 74, 528–534 (2017).
Chekroud, A. M. et al. Reevaluating the efficacy and predictability of antidepressant treatments: a symptom clustering approach. JAMA Psychiatry 74, 370–378 (2017).
Kraemer, H. C., Wilson, G. T., Fairburn, C. G. & Agras, W. S. Mediators and moderators of treatment effects in randomized clinical trials. Arch. Gen. Psychiatry 59, 877–883 (2002).
Papakostas, G. I. & Fava, M. Predictors, moderators, and mediators (correlates) of treatment outcome in major depressive disorder. Dialog. Clin. Neurosci. 10, 439–451 (2008).
Shelton, R. C. & Trivedi, M. H. Moderators of antidepressant response in major depression. J. Clin. Psychiatry 72, e32 (2011).
Kraemer, H. C., Yesavage, J. A., Taylor, J. L. & Kupfer, D. How can we learn about developmental processes from cross-sectional studies, or can we? Am. J. Psychiatry 157, 163–171 (2000).
McGrath, C. L. et al. Pretreatment brain states identify likely nonresponse to standard treatments for depression. Biol. Psychiatry 76, 527–535 (2014).
Konarski, J. Z. et al. Predictors of nonresponse to cognitive behavioural therapy or venlafaxine using glucose metabolism in major depressive disorder. J. Psychiatry Neurosci. 34, 175–180 (2009).
Arnow, B. A. et al. Depression subtypes in predicting antidepressant response: a report from the iSPOT-D Trial. Am. J. Psychiatry 172, 743–750 (2015).
Nolen-Hoeksema, S. Responses to depression and their effects on the duration of depressive episodes. J. Abnorm Psychol. 100, 569–582 (1991).
Kendler, K. S., Ohlsson, H., Keefe, R. S. E., Sundquist, K. & Sundquist, J. The joint impact of cognitive performance in adolescence and familial cognitive aptitude on risk for major psychiatric disorders: a delineation of four potential pathways to illness. Mol. Psychiatry 23, 1076–1083 (2018).
Utilizing the DSM-5.. Anxious distress specifier to develop treatment strategies for patients with major depressive disorder. J. Clin. Psychiatry 78, 1351–1362 (2017).
Bartova, L. et al. Reduced default mode network suppression during a working memory task in remitted major depression. J. Psychiatr. Res. 64, 9–18 (2015).
Etkin, A. et al. A cognitive-emotional biomarker for predicting remission with antidepressant medications: a report from the iSPOT-D trial. Neuropsychopharmacology 40, 1332–1342 (2015).
Roiser, J. P., Elliott, R. & Sahakian, B. J. Cognitive mechanisms of treatment in depression. Neuropsychopharmacology 37, 117–136 (2012).
Callicott, J. H. et al. Physiological characteristics of capacity constraints in working memory as revealed by functional MRI. Cereb. Cortex. 9, 20–26 (1999).
Disner, S. G., Beevers, C. G., Haigh, E. A. & Beck, A. T. Neural mechanisms of the cognitive model of depression. Nat. Rev. Neurosci. 12, 467–477 (2011).
Anticevic, A., Repovs, G., Shulman, G. L. & Barch, D. M. When less is more: TPJ and default network deactivation during encoding predicts working memory performance. Neuroimage 49, 2638–2648 (2010).
Klomp, A. et al. Test-retest reliability of task-related pharmacological MRI with a single-dose oral citalopram challenge. Neuroimage 75, 108–116 (2013).
Cao, H. et al. Test-retest reliability of fMRI-based graph theoretical properties during working memory, emotion processing, and resting state. Neuroimage 84, 888–900 (2014).
Plichta, M. M. et al. Test-retest reliability of evoked BOLD signals from a cognitive-emotive fMRI test battery. Neuroimage 60, 1746–1758 (2012).
Michl, J. et al. A multivariate approach linking reported side effects of clinical antidepressant and antipsychotic trials to in vitro binding affinities. Eur. Neuropsychopharmacol. 24, 1463–1474 (2014).
Wittchen H., Wunderlich U., Gruschwitz S., Zaudig M. SKID-I Strukturiertes Klinisches Interview für DSM-IV. Göttingen: Hogrefe; 1997.
Montgomery, S. A. & Asberg, M. A new depression scale designed to be sensitive to change. Br. J. Psychiatry 134, 382–389 (1979).
Bernal-Rusiel, J. L. et al. Statistical analysis of longitudinal neuroimage data with linear mixed effects models. Neuroimage 66, 249–260 (2013).
Chen, G., Saad, Z. S., Britton, J. C., Pine, D. S. & Cox, R. W. Linear mixed-effects modeling approach to FMRI group analysis. Neuroimage 73, 176–190 (2013).
Mumford, J. A. & Poldrack, R. A. Modeling group fMRI data. Soc. Cogn. Affect Neurosci. 2, 251–257 (2007).
Deyi, B. A., Kosinski, A. S. & Snapinn, S. M. Power considerations when a continuous outcome variable is dichotomized. J. Biopharm. Stat. 8, 337–352 (1998).
Riedel, M. et al. Response and remission criteria in major depression—a validation of current practice. J. Psychiatr. Res. 44, 1063–1068 (2010).
Dawes, R. M. The robust beauty of improper linear models in decision making. Am. Psychol. 34, 571–582 (1979).
Zimmerman, M., Martinez, J. H., Young, D., Chelminski, I. & Dalrymple, K. Severity classification on the Hamilton Depression Rating Scale. J. Affect Disord. 150, 384–388 (2013).
Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006).
Dunlop, B. W. et al. Functional connectivity of the subcallosal cingulate cortex and differential outcomes to treatment with cognitive-behavioral therapy or antidepressant medication for major depressive disorder. Am. J. Psychiatry 174, 533–545 (2017).
Margulies, D. S. et al. Mapping the functional connectivity of anterior cingulate cortex. Neuroimage 37, 579–588 (2007).
Cohen, J. A power primer. Psychol. Bull. 112, 155–159 (1992).
Bland, J. M. & Altman, D. G. A note on the use of the intraclass correlation coefficient in the evaluation of agreement between two methods of measurement. Comput. Biol. Med. 20, 337–340 (1990).
Hamilton, J. P. et al. Default-mode and task-positive network activity in major depressive disorder: implications for adaptive and maladaptive rumination. Biol. Psychiatry 70, 327–333 (2011).
Roy, M. et al. Medial prefrontal cortex activity during memory encoding of pictures and its relation to symptomatic improvement after citalopram treatment in patients with major depression. J. Psychiatry Neurosci. 35, 152–162 (2010).
Savli, M. et al. Normative database of the serotonergic system in healthy subjects using multi-tracer PET. Neuroimage 63, 447–459 (2012).
Anderson, I. M., McKie, S., Elliott, R., Williams, S. R. & Deakin, J. F. Assessing human 5-HT function in vivo with pharmacoMRI. Neuropharmacology 55, 1029–1037 (2008).
Scharinger, C. et al. Platelet serotonin transporter function predicts default-mode network activity. PLoS One 9, e92543 (2014).
Mayberg, H. S. et al. Regional metabolic effects of fluoxetine in major depression: serial changes and relationship to clinical response. Biol. Psychiatry 48, 830–843 (2000).
Davidson, R. J., Irwin, W., Anderle, M. J. & Kalin, N. H. The neural substrates of affective processing in depressed patients treated with venlafaxine. Am. J. Psychiatry 160, 64–75 (2003).
Pizzagalli, D. A. et al. Pretreatment rostral anterior cingulate cortex theta activity in relation to symptom improvement in depression: a randomized clinical trial. JAMA Psychiatry 6, 547–554 (2018).
Bosch, O. G. et al. Sleep deprivation increases dorsal nexus connectivity to the dorsolateral prefrontal cortex in humans. Proc. Natl Acad. Sci. USA 110, 19597–19602 (2013).
Young, K. D., Bellgowan, P. S., Bodurka, J. & Drevets, W. C. Neurophysiological correlates of autobiographical memory deficits in currently and formerly depressed subjects. Psychol. Med. 44, 2951–2963 (2014).
Hieronymus, F., Emilsson, J. F., Nilsson, S. & Eriksson, E. Consistent superiority of selective serotonin reuptake inhibitors over placebo in reducing depressed mood in patients with major depression. Mol. Psychiatry 21, 523–530 (2016).
Martin, R. E. & Ochsner, K. N. The neuroscience of emotion regulation development: implications for education. Curr Opin. Curr. Opin. Behav. Sci. 10, 142–148 (2016).
Etkin, A., Buchel, C. & Gross, J. J. The neural bases of emotion regulation. Nat. Rev. Neurosci. 16, 693–700 (2015).
Gyurak, A., Gross, J. J. & Etkin, A. Explicit and implicit emotion regulation: a dual-process framework. Cogn. Emot. 25, 400–412 (2011).
Gorlyn, M. et al. Neuropsychological characteristics as predictors of SSRI treatment response in depressed subjects. J. Neural Transm. 115, 1213–1219 (2008).
Leuchter, A. F. et al. Pretreatment neurophysiological and clinical characteristics of placebo responders in treatment trials for major depression. Psychopharmacology 177, 15–22 (2004).
Chang, H. H. et al. Treatment response and cognitive impairment in major depression: association with C-reactive protein. Brain Behav. Immun. 26, 90–95 (2012).
Denny, B. T. & Ochsner, K. N. Behavioral effects of longitudinal training in cognitive reappraisal. Emotion 14, 425–433 (2014).
Wise, T., Cleare, A. J., Herane, A., Young, A. H. & Arnone, D. Diagnostic and therapeutic utility of neuroimaging in depression: an overview. Neuropsychiatr. Dis. Treat. 10, 1509–1522 (2014).
Trusheim, M. R., Berndt, E. R. & Douglas, F. L. Stratified medicine: strategic and economic implications of combining drugs and clinical biomarkers. Nat. Rev. Drug Discov. 6, 287–293 (2007).
Schmidt, H. D., Shelton, R. C. & Duman, R. S. Functional biomarkers of depression: diagnosis, treatment, and pathophysiology. Neuropsychopharmacology 36, 2375–2394 (2011).
Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S. & Baker, C. I. Circular analysis in systems neuroscience: the dangers of double dipping. Nat. Neurosci. 12, 535–540 (2009).
Whitfield-Gabrieli, S. et al. Brain connectomics predict response to treatment in social anxiety disorder. Mol. Psychiatry 21, 680–685 (2016).
Alexopoulos, G. S. Personalizing the care of geriatric depression. Am. J. Psychiatry 165, 790–792 (2008).
We are thankful for critical comments and suggestions to Thomas Blank and Helena Chmura Kraemer. We also thank Manuel Kuhn, Christian Scharinger and Ana Weidenauer for their clinical work and patient evaluation.
L.B., B.M.M., L.P., and U.R. designed research. L.B., C.B., G.C., J.H., K.K., S.K. B.M.M., E.M., L.P., J.P., U.R., A.F.S., P.S. performed research (acquisition, analysis, and interpretation of data). B.M.M., L.P., and U.R. wrote the paper. L.B., C.B., G.C., J.H., K.K., S.K., E.M., J.P., A.F.S., and P.S. critically revised the paper. L.P. had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. This study was funded by the Oesterreichische Nationalbank, Anniversary Fund (Project number 13903) and the Austrian Science Fund (FWF). Clinical Research program (Project number KLI148-B00).
Siegfried Kasper received grants/research support, consulting fees and/or honoraria within the last three years from Angelini, AOP Orphan Pharmaceuticals AG, Celegne GmbH, Eli Lilly, Janssen-Cilag Pharma GmbH, KRKA-Pharma, Lundbeck A/S, Mundipharma, Neuraxpharm, Pfizer, Sanofi, Schwabe, Servier, Shire, Sumitomo Dainippon Pharma Co. Ltd. and Takeda. Alan F. Schatzberg reports conflicts of interest. Consultant: Bay City Capital, CeNeRx, Cervel, Clintara, Depomed, Eli Lilly, Forum (EnVivo), Genentech, Gilead, Jazz, Lundbeck/Takeda, McKinsey, MSI, Myriad Genetics, Naurex, Neuronetics, One Carbon, Pfizer, PharmaNeuroBoost, Sunovion, Xhale. Intellectual property: Named inventor on pharmacogenetic use patents on prediction of antidepressant response and glucocorticoid antagonists in psychiatry. Equity: Amnestix, CeNeRx, Corcept (co-founder), Forest Labs, Gilead, Incyte, Merck, Neurocrine, Pfizer, PharmaNeuroBoost, Seattle Genetics, Synosia, Titan, Xhale. The remaining authors declare that they have no conflict of interest.


text:
Q: Increasing the heap size in a traditional Websphere Application Server (WAS) 8.5 using the administration console? Heap dump when trying to run a large EAR application. Need to increase heap size. It is not a liberty server.

A: Found the answer through the administrative console.

Login to Administration console
Expand servers -> Websphere application servers -> Click on your server name (eg: server1)
Expand Java and Process Management under Server Infrastructure (in configuration tab)
Click on Process definition -> Java virtual machine
Update Initial heap size & Maximum heap size as required.
Apply and save the changes
Restart the Server

.


Canadian bond investors can exhale. The yield curve is back to normal, for now, following several weeks in which it had inverted, flashing signals of an impending recession.
The yield on the three-month bill has dipped 3 basis points below the 10-year yield, according to data compiled by Bloomberg. The gap inverted March 21 for the first time in more than a decade, sparking concern that a global economic slowdown and rising household debt would push the economy into a contraction. Longer term yields are generally higher than short-term notes.
The pick up of government bond yields is supporting an increase in corporate bond issues, with companies including the Hydro One Ltd. and Pempina Pipeline Corp. taking advantage of the increase of investor demand to advance their funding plans.


text:
Cabot Wealth Still 'Connecting the Dots' for its Customers 50 Years Later
August 07, 2020 by Ronn
What makes good marketing? It's a simple question but a crucial one, especially in the unique times we've all been trying to navigate the last four-plus months. Do we send more email or less email? Do we strive for clever copywriting and catchy phrases or caring and straight talk? Do we look to do more campaigns, tell more stories or both?
When a news release arrived a few weeks ago titled 7 Launches in 9 Months for Cabot Wealth, it presented a good opportunity for some answers.
"Being clever is way down on the list," said Ed Coburn, president of Cabot Wealth Network, which in September will mark its 50th year of advising individual investors. "It's not about clever copywriting or catchy phrases. A big part of it is understanding who we're selling to and what they're going through and what they're trying to accomplish, and writing copy that appeals to that need. Connecting the dots between what they want and what we can do for them."
Coburn is not a fan of the recent trend of short bulleted marketing copy. "We're in the consumer marketing field, where it's never become fashionable to use short copy. People who are not going to buy from you don't have time, but the people who want to buy, they want to read. Selling subscription products requires long copy that tells a story and makes an emotional connection."
He said that one strategy change has been the implementation of more campaigns—three, five, seven days in a row on one topic, "in different bites but building that case over a number of emails. It's worked well for us. We were concerned about emailing too much but people seem happy to engage. And the campaigns seem to give us a much better chance at selling. We're very systematic in that we track the results, what worked and what didn't. And if it works, we'll use that campaign again. We don't feel the need to recreate the wheel, but we are constantly refreshing."
That "refresh-ment" also shows in the product success. Those seven launches included: a limited membership options trading advisory service called Jacob's Private Circle with a price tag of $10,000 per year that sold out in just 10 hours; Financial Freedom, a digital magazine on financial wellness, that, along with an extensive library of reports and investor briefings, is part of a Gold Membership (though an annual Financial Freedom Federation membership costs just $10.); the Cabot Income Advisor; and the Cabot Retirement Club.
Talk about not resting on your laurels.
"These new services will generate substantial growth for us this year, on top of growing our existing services, and bring the total number of subscription products to 18 from 11," Coburn said.
"We're rooted in editorial. Carlton Lutts [father of current CEO Tim Lutts], was a subject matter expert in investing, as is Tim. So we always had products that came from real systems and were validated in their use. That was critically important. But now, the battle is much more about marketing."
And this is also where a winning history comes in. Coburn spoke about the customers who have been with them for "10, 15, 20, 30 years at a time—not a lot of people can say that." And even though that loyalty is "rooted in product," it was clear that they had to step up the marketing. "We weren't marketing in a way the products deserved. That's part of why Tim was attracted to me as a buyer." (Coburn joined Cabot in 2018.)
It's all quite astounding for an internal staff of just 14. "We're a small team so this has been a lot," Coburn said. "It's forced us to figure out where we best add value and how to leverage the resources of outside firms and freelancers. That will really help us continue our growth in the future. We plan to take a little bit of a breather while we work on our annual investor conference, the Cabot Wealth Summit, which will be held entirely online Aug 18-20."
Cabot staff tries to have dialogues with its audience whenever possible, through surveys with their subscription products and tracking the webchats or phone calls that editors partake in so they can identify trends. Of course, in olden times, they would have talked to them at their conferences, "spending time with our existing customers and asking people like them who aren't yet customers what they're looking for and what do they want to see," Coburn said.
"We're not the most aggressive publisher in the investment space when it comes to marketing and making outlandish claims. Our focus is providing investors with excellent investment advice using proven systems we have developed over years and decades. So it's nice to know that almost 50 years and hundreds of thousands of customers later, our reputation and excellent team of investment analysts are still producing the results for our subscribers and reaping benefits for Cabot as well."


text:
Pongal meaning "boiling over" is celebrated in Tamil Nadu with a lot of reverence, joy and fervor and also as an occasion to express gratitude to nature.
While students dressed up in beautiful traditional attire, the schools were decorated with colorful rangolis. Some schools even conducted rangoli competitions.
In keeping with the auspicious symbols of wellbeing and Pongal celebrations – sun, sugarcane, bullocks, open wood fire, and overflowing pots of milk, most schools cooked sweet Pongal on an open fire in the playground with music and dance
Both students and children participated in traditional games like kabadi, uriadithal (pot breaking) and other games like tug–of–war with vigor and joy.
Dharmapuri School's science teacher conducted a social science activity to bring awareness about the functioning of the village Panchayat and District Collector's role in developing villages.
6th Std students researched the functions of the village panchayat and the duties of village representatives and participated in a village council meeting where they discussed their village problems and put forth developmental plans for their villages.
Selected students acted as the village panchayat with the teachers guiding them about the day–to–day functioning, responsibilities and problems handled.
This activity was very useful in empowering students with knowledge on how the village administration functions.
Muthu Selvan S., a student at the Tuticorin school is sponsored by Ms. Shibani Balvally. She gifted him a pack of 12 balls to celebrate his birthday on 18th January. His happiness had to be seen to be believed. Thank you Shibani for making his birthday special!
The Protection of Children from Sexual Offences Act 2012 (POCSO Act) was formulated to effectively address sexual abuse and sexual exploitation of children. As children spend a lot of time at school, a safe learning environment is essential for students of all ages.
Our coordinator, Ms. Nandhini, and our HR manager, Mr. Prakash, conducted a 2–day workshop for the Cuddalore school staff and students where they spoke about children's rights and the need to sensitize everyone involved about safety for children, be it physical or psychological safety. They also spoke about the schools' operating plan and policies for the safety of students, and regular safety audits.
Other relevant modules will also be conducted to spread awareness about POCSO, not only among children but also among teachers, parents and support staff, so that everyone is equally aware and well-equipped to support the children.
200 supporters and 170 employees from 7 corporates participated in the Tata Mumbai Marathon on 21st January 2018 to create awareness and raise funds for Isha Vidhya. Isha brahmacharis also participated once again and drew a lot of curiosity and attention with their calm demeanor and traditional attire.
We held several volunteering sessions across Chennai in January 2018. Several volunteers participated in the chart–making events and in the process, learnt more about the teaching methodology followed in our schools.
Vaishnavi from Thambaram shares, "You could feel how everyone was transformed into children during the activity. Just looking at the colours and patterns everyone used in the charts showed how much of a child we have inside of us. All of us learnt that even from our homes, and in small activities, we can experience child–like joy."
We are willing to offer this opportunity in various cities to supporters who want to volunteer for Isha Vidhya. To learn more, please send a mail to iyyappan.arumugam@ishavidhya.org.
ishavidhya.org | Contact us | Privacy | Unsubscribe


Sounds like a very inspiring workshop that produced some wonderful work. Oh, and cute hat!
Is that a picture of you? Cute! I understand water color is very difficult. Great job! Thanks for sharing.
Thank you both for your comments. It was a very inspiring workshop. And yes, Sheri, c'est moi, on the weekend in Carré St. Louis in Montreal–in my new favourite hat!


Hi, i'm using prom lite 1.1, after import an .csv file and transform to xes the information showed to me are duplicated.
The process below has the id '1234' with 14 line and diferents activities and timestamp.
I guess each line (one activity) is mapped to two events in the log, i.e., we mean start and complete. If not, please put your csv here.
Thanks for the answer, I have one activity that occur many times but in different dates.
I alread did a lot of tests, even changing the name of the activities the problem persists.
I did it with the following setting and it works fine with just 14 events.
The problem was the "Start time". I did not fill out and it worked.
I always used the same data in this two fields, I do not know if it was wrong.


text:
Junior League of Columbus initiates poverty awareness in Muscogee Co.
By WTVM Web Team | April 6, 2016 at 3:33 PM EDT - Updated July 9 at 6:09 PM
COLUMBUS, GA (WTVM) – The Junior League of Columbus is partnering with the Little Black Dress Initiative this week to bring awareness of poverty in the local area.
This initiative encourages people to wear the same black dress every day for poverty. Around 23 percent of children in the Muscogee County live in poverty and Georgia is ranked sixth in the United States.
Muscogee County School Board member Kia Chambers and the Leadership Institute at Columbus State University are some of the people participating in this initiative.
If you would like to contribute to the Junior League of Columbus to help children in poverty, you can drop off diapers or cash at the Wynn House, located at 1240 Wynnton Road.
Copyright 2016 WTVM. All rights reserved.
Each kid had $100 to spend at Academy Sports and Outdoors for shoes, clothes and sports equipment.
NBC polls voters on issues ahead of 2020 election
Madison McClung
Special Report: Fake Filters
Former St. Vincent's Academy teacher, track coach indicted on 22 charges
Columbus restaurants to offer specialty menus during Restaurant Week
Brennan Reh


After closely monitoring the success former teammates Tristan Thompson and Cory Joseph have had as Longhorns, Kevin Thomas has decided to follow in their footsteps and will join them at Texas next season. Coach Rick Barnes is locked in north of the border as Thomas becomes the 4th Canadian to pledge to the Burnt Orange in the last two years. Also a member of the Longhorns 2011 recruiting class is Findlay Prep point guard Myck Kabongo, who's also KT's AAU teammate.
According to Ro Russell who coaches Kevin both at CFCA in North Carolina and on the summer circuit with Grassroots Canada, "Kevin started to become more and more interested as they started to recruit him. He had two assistant coaches and head coach Rick Barnes come see him play. He had been speaking to Tristan and Cory who gave him a great report on the program. With forwards leaving due to graduation and possibly the NBA, he visited them with his family and they were all convinced it was the perfect spot for him.
Kevin is a great pickup for the Longhorns. He's a hybrid forward with tremendous athleticism and versatility. He is outstanding in the open court and can really finish at the rim. He can also shoot it, make plays off the bounce facing the basket, and is capable of guarding multiple positions.
Don't believe us? Check out these highlights of Kevin from this years REDA Christmas Showcase.


text:
Match Preview: Durham Jets v Lancashire Lightning
Match Preview: Durham Jets v Lancashire Lightning, NatWest T20 Blast
Get ready for Blast off!
Lancashire Lightning begin their quest for a second NatWest T20 Blast title in three years with a trip to the North East to take on Durham Jets under lights at Emirates Riverside tomorrow.
These two sides have played out some crackerjack battles in the Blast over the years, with both sides sharing a win apiece in last season's group stage.
Buy NatWest T20 Blast tickets
Lightning are confident of adding their name to the short list of teams who have won this competition more than once.
Leicestershire have won it three times, while Hampshire and Northants have won it twice apiece.
It is the first of two North Group matches in three days for the Lightning, who host Leicester at Liverpool on Sunday afternoon.
Durham Jets v Lancashire Lightning
Emirates Riverside
Durham reached only their second Finals Day and their maiden final last year, but were pipped to the title by Northants.
The Jets finished fourth in the North and one place and a point above the Lightning.
They won their away quarter-final against Gloucestershire and semi against Yorkshire at Edgbaston before Northants reeled in a 154 target to win by four wickets.
Durham start this year's competition minus four points following ECB penalties imposed due to the county's financial problems.
New Zealand batsman Tom Latham has replaced South Africa Test opener Stephen Cook for the second half of the summer to play in the Blast and the County Championship as their overseas.
Left-handed Latham, 25, was on Durham's Academy in 2010 and has made himself an integral part of his country's Test and one-day sides. He played the last of 12 T20 internationals two years ago.
LAST TIME OUT
Lancashire ended last season's competition with back-to-back wins, the first of which came against the Jets at the Riverside.
A trio of scores in the forties from Alviro Petersen, Karl Brown and Liam Livingstone led the Lightning to 176-3 having been inserted.
Durham were 115-2 in the 15th over until Saqib Mahmood claimed the wickets of Callum MacLeod and Ryan Pringle in three balls.
He finished with 3-31 before Nathan Buck defended 18 off the last over to seal a four-run win.
Dane Vilas wants Lancashire to learn from their one-day mistakes of earlier in the season and make a fast start to the Blast.
The Lightning narrowly missed out on a place in the RL50 knockout stages having lost their first three group matches.
"Hopefully we'll have a better start to our campaign than we had in the Royal London," said the in-form South African batsman.
"We started off with those three losses, which put us on the back foot and meant we had to play catch up."
Vilas has previous experience of success in T20 having won South Africa's domestic competition with Cape Cobras in 2010/11.
"I've been really lucky to have done well with the Cobras and play in some really good teams there," he said.
"We went to the Champions League in India three times having reached the finals or won the competition back home."
Vilas is not the only one confident of success this season, with coach Glen Chapple admitting: "We've got a lot of quality, and I'm very optimistic about our chances.
"As ever, a single T20 game, you can't predict it. But if you play good enough cricket throughout the full group, you should have a good chance of qualifying. A good start's crucial.
"Our record of getting through to the quarters is good. That's the first challenge. Then we'll look to improve our ratio in the knockout stages."
NatWest T20 Blast tickets
Don't miss out on the biggest summer's day out of the year and secure your tickets to watch the NatWest T20 Blast with tickets from £10 for adults and just £1 for kids.
Packed into just three short hours of explosive T20 cricket, the NatWest T20 Blast offers plenty of entertainment on and off-the-field for the entire family with themed nights, live music, street performers, games and activities, fancy dress stalls and plenty of food and drink in our Foster's Fan Village.
Plus, with some of the biggest names in international cricket joining Lancashire this summer you can expect to see plenty of wickets flying and huge sixes at every match.
Lancashire Lightning NatWest T20 Blast home fixtures:
Lancashire Lightning v Leicestershire Foxes, Sun 9 July, Liverpool CC - Buy Tickets
Lancashire Lightning v Yorkshire Vikings, Fri 14 July, Emirates Old Trafford
Lancashire Lightning v Derbyshire Falcons, Sun 16 July, Emirates Old Trafford - Buy Tickets
Lancashire Lightning v Durham Jets, Sun 23 July, Emirates Old Trafford - Buy Tickets
Lancashire Lightning v Notts Outlaws, Fri 28 July, Emirates Old Trafford - Buy Tickets
Lancashire Lightning v Worcestershire Rapids, Wed 16 August, Emirates Old Trafford - Buy Tickets
Lancashire Lightning v Birmingham Bears, Fri 18 August, Emirates Old Trafford - Buy Tickets
Hurry and buy your tickets now. You can buy yours tickets here, or by calling 08444 999 666* or emailing tickets@lccc.co.uk.
*Calls to 08444 999 666 cost 5p per minute from a BT landline, other networks and mobiles will vary.


Ferindonald or Ferrindonald (Scottish Gaelic: Fearann Dòmhnaill) is a crofting township, located on the east coast of the Sleat peninsula, lying on the A851 road, in the Isle of Skye, Scottish Highlands and is in the Scottish council area of Highland.
Ferindonald is located 0.25 miles southwest of Saasaig and 1.5 miles of Teangue.
^ "Ferindonald". The Gazetteer for Scotland. School of GeoSciences, University of Edinburgh and The Royal Scottish Geographical Society. Retrieved 19 June 2018.
Wikimedia Commons has media related to Ferrindonald.
This page was last edited on 19 June 2018, at 11:33 (UTC).


text:
2022 Outlook: Global equities
Robust performance expected from mid-sized companies
We expect global mid-cap equities to generate sustainable and healthy returns, with investors paying more attention to fundamentals versus the momentum-driven recent past. In 2022, we will consider the risks posed by inflation and continue to seek strong industry structures, capable management teams with capital discipline, and clear valuation-driven opportunities.
Download 2022 Outlook
The Global Future Leaders strategy scans the global small and mid-cap universe for the leaders of tomorrow and explores a range of themes that are expected to experience structural growth. We have an acute focus on sustainability, pricing power, market structures, brand strength, product differentiation and valuation discipline.
Within the portfolio context, we think about environmental, social, and governance (ESG) factors both holistically and at an individual stock level to find businesses with a long runway of growth that can underpin robust outcomes for our clients.
What is your investment outlook in 2022?
We expect global mid-cap equities to continue generating healthy returns for investors. That said, we expect more moderate and sustainable gains relative to the bottom of the pandemic-induced crash of March 2020. The initial phase of a market upturn is usually driven by expansion, as investors anticipate the recovery. This period usually produces explosive gains. However, as the economy recovers and business fundamentals improve, the earnings recovery takes over as the primary driver of shareholder returns. This is where we are now. There is usually some increase in volatility, too, as we move into this phase, and it is quite typical to have mild drawdowns from time to time.
The benefit that the equity market still holds is its real earnings recovery and growth outlook relative to low-yielding bonds.
What do you think could surprise the market in 2022, either positively or negatively?
In our 2021 outlook, we cautioned that inflation was likely to pick up, and it did. Some of this was due to temporary or transient factors that would subside over time once the lockdowns ended and manufacturing and global supply chains normalised. In recent weeks, company management teams have warned about rising labour costs, which tend to be sticky. Labour cost is a key component of inflation, alongside housing. Bond markets have already anticipated that central banks will need to act sooner than they had previously communicated. However, equity markets have yet to adjust, as rising yields impact equity valuations, while increased labour costs can impact corporate profit margins. This could lead to some choppiness and volatility as the equity markets digest this new reality.
Our holistic investment approach in 2022 will consider the risk of broad-based inflation, higher debt costs, rising commodity and property prices, and tighter labour markets, as well as increasing wages and higher levels of competition.
Which themes, sectors, or regions offer opportunities or potential risks?
The Global Future Leaders strategy scans the global small and mid-cap universe for the leaders of tomorrow and explores a range of themes that are expected to experience structural growth. The table below provides insight into the sectors and associated themes that we are exploring.
Global Future Leaders' Themes
Data centres and cloud
Subscription-led content models
Connectivity enablers and 5G
Energy, Resources, and Utilities
Wind & hydro
Mass market luxury
Global exchanges
Trust and advisory
Customised insurance
Contact development outsourcing
Within your portfolio, where do you have the highest levels of conviction, and which areas are you avoiding?
We believe that some of the best opportunities lie in high-quality companies with competitive advantages in good industry structures. These are managed by competent individuals and bought at reasonable prices. Such businesses will have some inherent pricing power, allowing them to protect margins by passing on increases in labour costs to consumers. Areas that we would deem vulnerable are loss-making businesses or those with valuations that exceed their peers. The former require access to outside capital to keep funding their aggressive growth ambitions at a time when this funding is becoming more expensive. At the same time, the latter may experience significant corrections in their stock prices as discount rates continue to increase.
We will maintain an acute focus on sustainability, pricing power, market structures, brand strength, product differentiation and valuation discipline to manage 2022 well.
Do you expect sustainability factors to influence returns, and how is this reflected in your portfolios?
Sustainability is one of the three key pillars we use to assess companies (the other two being viability and credibility). In looking for the future leaders of tomorrow, we are primarily interested in finding firms that operate under sustainable business models. In our view, businesses that severely harm the environment fail to respect their employees, customers, or society at large. Furthermore, these companies do not honour the rights of minority shareholders and are filled with executives whose primary aim is to enrich themselves at the owners' expense. In our view, these names have a finite corporate life. Within the context of the portfolio and at an individual stock level, we think about ESG factors holistically. For example, we will refuse to invest in harmful corporations that include tobacco or cluster munitions whilst simultaneously investing in businesses that enable clean energy (e.g. solar). Our carbon footprint at a total portfolio level is a fraction of that seen in our benchmark. In aggregate, our portfolio's ESG rating is relatively superior to that of our universe.
Read full disclaimer
This document is issued by FIL Responsible Entity (Australia) Limited ABN 33 148 059 009, AFSL No. 409340 ("Fidelity Australia"). Fidelity Australia is a member of the FIL Limited group of companies commonly known as Fidelity International.
Prior to making an investment decision, retail investors should seek advice from their financial adviser. This document is intended as general information only. Please remember past performance is not a guide to the future. Investors should also obtain and consider the Product Disclosure Statements ("PDS") fo r the fund(s) mentioned in this document before making any decision about whether to acquire the product. The PDS is available on www.fidelity.com.au or can be obtained by contacting Fidelity Australia on 1800 119 270. The Target Market Determination (TMD) for Fidelity Australian product(s) is available at www.Fidelity.com.au. This document has been prepared without taking into account your objectives, financial situation or needs. You should consider such matters before acting on the information contained in this document. This document may include general commentary on market activity, industry or sector trends or other broad-based economic or political conditions which should not be construed as investment advice. Information stated herein about specific securities is subject to change. Any reference to specific securities should not be construed as a recommendation to buy, sell or hold these securities. While the information contained in this document has been prepared with reasonable care, no responsibility or liability is accepted for any errors or omissions or misstatements however caused. The document may not be reproduced or transmitted without prior written permission of Fidelity Australia. The issuer of Fidelity's funds is FIL Responsible Entity (Australia) Limited ABN 33 148 059 009. Details of Fidelity Australia's provision of financial services to retail clients are set out in our Financial Services Guide, a copy of which can be downloaded from our website.
© 2021 FIL Responsible Entity (Australia) Limited. Fidelity, Fidelity International and the Fidelity International logo and F symbol are trademarks of FIL Limited
James Abela
Portfolio Manager, Fidelity's Future Leaders strategies
Maroun Younes
Co-Portfolio Manager Fidelity Global Future Leaders Fund and Analyst
2022 Outlook: Emerging Markets
2022 Outlook: Sustainability
2022 Outlook: Global Demographics
2022 Outlook: China


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                    // views do not consist of columns just SQL queries
                    ->write('
', $this->getRawViewName(), $this->getModelName(), $namespace)
                    ->write('
')
                ->outdent()
            ;

            return self::WRITE_OK;
        }

        return self::WRITE_EXTERNAL;
    }
}


Director of Intech Plumbing & Construction is a licensed plumber and a registered builder that pays particular attention to detail from the design aspect of the project to the finished product as per the clients requirements. Commitment to the quality of materials and workmanship is of the highest standard to ensure that Intech Plumbing & Construction delivers professional quality domestic and commercial results, in client service, the construction process, effective pricing and outstanding performance all round.
Whether it's a design assist, design construct or plan spec basis for your project, Intech Plumbing & Construction has the resources to serve you with particular attention to detail from start to finish, including ongoing maintenance to prolong the life of your project.
It is our commitment to provide you with the correct plumbing and building process for new construction, renovation/re-model and just general improvements around the home to make it a more environmentally sustainable residence.


A little place on the web for idle musings, hair-brained ideas, and found objects.
Link: Elon Musk asking his followers on Twitter how Tesla can improve, and then replying to them deeper into the thread as he gets things resolved.
Idea: A glass wind chime that mounts on your dash, and indicates by dinging (or breaking, natch) if you're driving too haphazardly.


text:
Пользовательское соглашение

	Заходя на сайт «{v#constsitename}» (в дальнейшем «мы», «наш», «{v#constsitename}», «{v#consthost}»),
	вы подтверждаете своё согласие со следующими условиями. Если вы не согласны с ними,
	пожалуйста, не заходите и не пользуйтесь сайтом «{v#constsitename}». Мы оставляем за собой
	право изменять эти правила в любое время и сделаем всё возможное, чтобы уведомить вас
	об этом, однако с вашей стороны было бы разумным регулярно просматривать этот текст на
	предмет изменений, так как использование сайта «{v#constsitename}» после обновления/исправления
	условий означает ваше согласие с ними.

	Наши сайт работает под управлением программного обеспечения для создания сайтов Платформа Абрикос
	(в дальнейшем «они», «программное обеспечение Платформа Абрикос», «abricos.org», «Abricos Group»),
	выпущенного по лицензии «MIT License» (в дальнейшем «MIT»).
	Скачать его можно по адресу http://abricos.org/.
    Ограничения лицензии MIT
	для программного обеспечения Платформа Абрикос строго связаны с организацией и поддержкой
	интернет-сайтов, и Abricos Group не несёт ответственности за то, что администрация сайта определяет
	в качестве допустимого содержания и/или поведения в них. За дополнительной информацией о Abricos Group
	обращайтесь по адресу http://abricos.org/.

	Вы соглашаетесь не размещать оскорбительных, угрожающих, клеветнических сообщений, порнографических
	сообщений, призывов к национальной розни и прочих сообщений, которые могут нарушить законы вашей страны,
	страны, которая предоставляет услуги хостинга для сайта «{v#constsitename}» или международное право.
	Попытки размещения таких сообщений могут привести к вашему немедленному отключению от сайта, при этом
	ваш провайдер будет поставлен в известность, если мы сочтём это нужным. IP-адреса всех сообщений сохраняются
	для возможности проведения такой политики. Вы соглашаетесь с тем, что администраторы сайта «{v#constsitename}»
	имеют право удалить, отредактировать, перенести или закрыть любое ваше сообщение в любое время
	по своему усмотрению. Как пользователь вы согласны с тем, что введённая вами информация будет храниться
	в базе данных. Хотя эта информация не будет открыта третьим лицам без вашего разрешения, ни администрация
	сайта «{v#constsitename}», ни Abricos Group не может быть ответственна за действия хакеров, которые могут
	привести к несанкционированному доступу к ней.


After the 31-year-old wrestler appeared on TNA's "Impact" last night, the Baltimore-based ROH released a lengthy statement.
"The contract Kenny King signed in June 2011 expired 11 days ago. Before that expiration, an agreement was reached between Kenny and ROH management for an extension of that contract, with a provision to allow him to negotiate with any other promotions so that he could evaluate his options and any potential interest in him elsewhere. When that short extension concluded he would then be free to accept an offer elsewhere, or sign another long term contract with ROH. Under this agreement, Kenny was free to negotiate but not to wrestle for another organization without ROH's permission," the statement explained.
VIDEO: Watch Kenny King make his return to TNA "Impact" below.