---
abstract: 'With the rapid development of digital multimedia, video understanding has become an important field. For action recognition, temporal dimension plays an important role, and this is quite different from image recognition. In order to learn powerful feature of videos, we propose a Collaborative Temporal Modeling (CTM) block (Figure \[fig1\]) to learn temporal information for action recognition. Besides a parameter-free identity shortcut, as a separate temporal modeling block, CTM includes two collaborative paths: a spatial-aware temporal modeling path, which we propose the Temporal-Channel Convolution Module (TCCM) with unshared parameters for each spatial position ($H \times W$) to build, and a spatial-unaware temporal modeling path. CTM blocks can seamlessly be inserted into many popular networks to generate CTM Networks and bring the capability of learning temporal information to 2D CNN backbone networks, which only capture spatial information. Experiments on several popular action recognition datasets demonstrate that CTM blocks bring the performance improvements on 2D CNN baselines, and our method achieves the competitive results against the state-of-the-art methods. Code will be made publicly available.'
address: 'iQIYI, Inc.'
bibliography:
- 'myref-mmm.bib'
title: 'CTM: Collaborative Temporal Modeling for Action Recognition'
---

Ł[[L]{}]{}

Action recognition, CNNs, Feature representation, Temporal modeling, CTM, TCCM

Introduction {#sec:intro}
============

As a result of the rapid development of digital multimedia, action recognition has become an important research field for video understanding, video surveillance, etc. For action recognition, temporal dimension plays an important role, which is different from image recognition, since that the information of a single frame is often not enough to recognize the action class. In this paper, we will focus on how to learn the temporal information from videos.

![The Collaborative Temporal Modeling (CTM) block. Besides a parameter-free identity shortcut, the CTM block includes two paths : Path 1 is a spatial-aware temporal modeling path: TCCM, and Path 2 is a spatial-unaware temporal modeling path. “$\bigoplus$" denotes element-wise addition.[]{data-label="fig1"}](f1.pdf){width="0.9\linewidth"}

Recently, action recognition has witnessed good improvements resulted from deep convolutional neural networks (CNNs), and the existing deep learning based approaches can be divided into two major categories: 2D CNNs based methods and 3D CNNs based methods. A lot of methods adopt 2D CNNs as the backbones for action recognition. 2D CNNs extract the spatial feature representation of each frame. The late fusion step is used for aggregating frame-wise high-level features to prediction. It is argued that late fusion ways cannot well learn the temporal information due to the high-level feature is very abstracted. Some late fusion ways (e.g. maximum pooling, average pooling) even do not learn the temporal information. 3D convolution kernel encodes the spatial and temporal features jointly. Some methods [@qiu2017learning; @tran2018closer; @xie2018rethinking] decompose 3D convolution kernel into separate spatial and temporal part. These 3D CNN based methods can learn spatio-temporal representation and achieve superior performance.

2D CNNs backbones have the advantage of low computational cost but only learn spatial information. 3D CNNs based methods learn spatio-temporal representation but have higher computational burdens than 2D CNNs. It is still an active field that find proper network architectures to learn the spatial and temporal feature from videos.

Let us focus on the temporal information learning ways. In most cases, a block (e.g. a P3D-A block) conducts both the temporal information learning and the spatial information learning. Although the spatial modeling and temporal modeling are factorized as different layers in some methods (we name this “layer decoupling", and a toy example is shown in Figure \[fig2\]), they are still operated in the same block. We expect that a separate temporal modeling block may have strong ability of learning temporal feature representation, e.g. the spatio-temporal encoding blocks are decomposed into separate spatial modeling blocks and separate temporal modeling blocks (we name this “block decoupling" and show a toy example in Figure \[fig2\]). The intuition here is that different blocks should not have the same modeling capacity, analogous to the human retina’s working mechanism that neurons (Parvo cells and Magno cells) in the retina do not have the same capabilities.

Besides, the parameters of a temporal filter are shared for each spatial position ($H \times W$) in most cases, thus the temporal filter is spatial-unaware. However, spatial-aware temporal modeling may be quite effective for action recognition, due to that the motion directions and magnitudes at different positions ($H \times W$) are different for two consecutive frames. We consider that the spatial-aware temporal modeling will be collaborative to the existing spatial-unaware temporal modeling.

Based on the above motivations, we propose the Collaborative Temporal Modeling (CTM) block to learn the temporal information for action recognition. It is a separate temporal modeling block. As shown in Figure \[fig1\], besides a parameter-free identity shortcut, CTM block includes two paths: Path 1 is a spatial-aware temporal modeling path shown in Figure \[fig3\], and Path 2 is a spatial-unaware temporal modeling path shown in Figure \[fig4\]. We propose a spatial-aware Temporal-Channel Convolution Module (TCCM) to build Path 1. The parameters of temporal filters in TCCM are unshared for each spatial position ($H \times W$). In CTM block, spatial-aware Path 1 and spatial-unaware Path 2 are collaborative to learn the temporal feature. The CTM blocks are flexible and can be inserted into many popular network architectures to generate CTM networks with very limited additional computational cost. Specifically, for 2D CNN backbones, which only have spatial modeling ability, CTM blocks bring the high capability of temporal modeling to them.

To summarize, the main contributions of our paper include: 1) We propose a simple yet effective Collaborative Temporal Modeling (CTM) block to operate separate temporal modeling. 2) We propose a Temporal-Channel Convolution Module (TCCM) to learn spatial-aware temporal encoding. 3) We propose CTM networks with high capability of learning spatial and temporal features for action recognition. 4) Experiments demonstrate that CTM blocks bring performance improvements on 2D CNN baselines and our method achieves the competitive results against the state-of-the-art methods on several action recognition datasets.

Related Work
============

Deep learning-based methods [@luo2019grouped; @wang2016temporal; @he2019stnet; @lin2019tsm; @jiang2019stm; @zolfaghari2018eco; @Chen_2018_ECCV] have superior performance than hand-crafted features and obtain remarkable success on some public datasets [@soomro2012ucf101; @kay2017kinetics; @Kuehne11HMDB]. Action recognition methods based on CNNs can be grouped into two categories: 2D CNNs based methods [@karpathy2014large; @wang2016temporal] and 3D CNNs based methods.

With the input frames which are selected from a video by some specific regulations, 2D CNNs operate on each frame independently. Temporal Segment Networks (TSN) [@wang2016temporal] is one of the representative 2D CNN methods. 2D CNNs extract the spatial feature of each frame, then the late fusion step can aggregate each frame’s spatial feature to prediction. Some late fusion ways (e.g. maximum pooling, average pooling) cannot learn the temporal information. Other late fusion ways specially designed for involving temporal relation often cannot well learn the temporal information. This may result from that the high-level feature is highly abstracted.

[0.45]{} ![Toy examples of layer decoupling and block decoupling. (a) a C3D block. (b) a P3D block. P3D decouples the 3D convolution layer into the spatial convolution layer and the temporal convolution layer. (c) a separate spatial modeling block. (d) a separate temporal modeling block. From (a) to (b) is a toy example of layer decoupling. From (b) to ((c) + (d)) is a toy example of block decoupling.[]{data-label="fig2"}](f2-a.pdf "fig:"){width="\linewidth" height="14ex"}

[0.45]{} ![Toy examples of layer decoupling and block decoupling. (a) a C3D block. (b) a P3D block. P3D decouples the 3D convolution layer into the spatial convolution layer and the temporal convolution layer. (c) a separate spatial modeling block. (d) a separate temporal modeling block. From (a) to (b) is a toy example of layer decoupling. From (b) to ((c) + (d)) is a toy example of block decoupling.[]{data-label="fig2"}](f2-b.pdf "fig:"){width="1.1\linewidth" height="15ex"}

[0.45]{} ![Toy examples of layer decoupling and block decoupling. (a) a C3D block. (b) a P3D block. P3D decouples the 3D convolution layer into the spatial convolution layer and the temporal convolution layer. (c) a separate spatial modeling block. (d) a separate temporal modeling block. From (a) to (b) is a toy example of layer decoupling. From (b) to ((c) + (d)) is a toy example of block decoupling.[]{data-label="fig2"}](f2-c.pdf "fig:"){width="\linewidth"}

[0.45]{} ![Toy examples of layer decoupling and block decoupling. (a) a C3D block. (b) a P3D block. P3D decouples the 3D convolution layer into the spatial convolution layer and the temporal convolution layer. (c) a separate spatial modeling block. (d) a separate temporal modeling block. From (a) to (b) is a toy example of layer decoupling. From (b) to ((c) + (d)) is a toy example of block decoupling.[]{data-label="fig2"}](f2-d.pdf "fig:"){width="\linewidth"}

3D convolution networks can effectively extract spatial-temporal information. C3D [@tran2014c3d] encodes the spatial and temporal features jointly via a 3D convolution kernel $D \times K \times K$, where $D$ is the temporal dimension and $K$ is the spatial size. I3D [@carreira2017quo] inflates parameters of pretrained 2D image classification models to 3D. The Channel-Separated Convolutional Network (CSN) [@tran2019video] replaces expensive 3D convolution operations by channel interactions and spatio-temporal interactions separately. Considering the high computational burdens of 3D convolution kernel, in some methods, such as Pseudo-3D [@qiu2017learning], R(2+1)D [@tran2018closer] and S3D [@xie2018rethinking], a 3D convolution kernel $D \times K \times K$ is decoupled into a spatial convolution kernel $1 \times K \times K$ and a temporal convolution kernel $D \times 1 \times 1$, and simulate a 3D convolution layer is decoupled to a spatial encoding layer and a temporal encoding layer. However, after such “layer decoupling" (a toy example is shown in Figure \[fig2\]), the spatial encoding layer and the temporal encoding layer are still operated in the same block. Besides, the parameters of temporal filters are not spatial-aware in most methods.

Approach
========

In this section, firstly Temporal-Channel Convolution Module (TCCM) in CTM is described. Then, CTM blocks and CTM networks are introduced.

![Temporal-Channel Convolution Module (TCCM). TCCM is “Path 1" of a CTM block. For feature maps $ {\bf F} $ with the size of $T \times C\times H \times W$, TCC operates on the $T$ and $C$ dimensions.[]{data-label="fig3"}](f3-math.pdf){width="0.95\linewidth"}

Temporal-Channel Convolution Module (TCCM) {#3-1-tccm}
------------------------------------------

At first, we describe the design of the Temporal-Channel Convolution (TCC) in TCCM. Consider the input feature maps with the size of $T \times C \times H \times W$ (we omit batch-size for simple), where $T, C, H, W$ are the temporal length, channels, height and width, respectively, TCC does not operate on the $H$ and $W$ dimensions. Instead, TCC operates on the $T$ and $C$ dimensions. TCC is designed for temporal modeling with unshared parameters for each spatial position ($H\times W$). Formally, the Temporal-Channel Convolution (TCC) can be formulated as: $${\bf O}^{h,w}_{t,c} = \sum_{i,j} {\bf K}^{h,w}_{i,j} {\bf F}^{h,w}_{t+i,c+j},$$\[eq1\] where $ {\bf K}^{h,w}_{i,j} $ denotes the filter for a spatial position ($h \times w$), the subscripts $i, j$ denote the temporal and channel indices of the kernel, $ {\bf F} $ is the input feature maps, and ${\bf O}$ is the output feature maps. Eq \[eq1\] can be implemented easily via combining some existing functions in some popular deep learning platforms. Specifically, the shape of feature maps is changed from $T \times C \times H \times W$ to $1 \times HW \times T \times C$ and the common depth-wise 2D convolution is operated on this feature maps, then the shape is changed back. The architecture of TCCM is shown in Figure \[fig3\]. Firstly, in order to decrease the computational cost, the 1x1 convolution layer is leveraged for reduce the number of channels $C_{1}$ of the input feature maps to $C_{2}$. Then, TCC is operated. Finally, restore the number of channels from $C_{2}$ to $C_{1}$. In this paper, the 2D TCC kernel size is set to $3 \times 3$, and the shape of kernel is $(H \times W,1,3,3)$.

Collaborative Temporal Modeling (CTM) block
-------------------------------------------

Figure \[fig1\] presents the architecture of the Collaborative Temporal Modeling (CTM) block. It is a separate temporal modeling block. Besides a parameter-free identity shortcut, CTM includes two collaborative temporal modeling paths. The TCCM path (Path 1) is spatial-aware temporal modeling module and is described in Section \[3-1-tccm\]. Another temporal encoding path (Path 2) is a module with the $3\times1\times1$ temporal convolution and this module is a spatial-unaware temporal modeling module, as illustrated in Figure \[fig4\]. In Figure \[fig4\], after reducing the number of channels $C_{1}$ of the input feature maps to $C_{2}$, the shape of feature maps is permuted to $C_{2} \times T \times H \times W$ to match the $3\times1\times1$ temporal convolution. Then, the shape is permuted back and the number of channels is restored from $C_{2}$ to $C_{1}$ after this $3\times1\times1$ temporal convolution layer. In Path 2, the parameters of $3\times1\times1$ temporal convolution are shared for each spatial position ($H \times W$). Path 1, Path 2 and a parameter-free identity shortcut are combined to build a CTM block.

![The architecture of “Path 2" in a CTM block.[]{data-label="fig4"}](f4.pdf){width="0.45\linewidth"}

![The overall architecture of CTM network. 2D ResNet-50 backbone is adopted as an example. Three CTM blocks (after the 1st, 3rd and 5th bottleneck block, respectively) are added to Res4. “Bottleneck" denotes a residual block of 2D ResNet-50 backbone and it is a separate spatial modeling block. “CTM" denotes a CTM block and it is a separate temporal modeling block. “Bottleneck" blocks and “CTM" blocks are combined in CTM networks to make CTM networks have high capability of spatial and temporal modeling.[]{data-label="fig5"}](f5.pdf){width="\linewidth"}

CTM Network
-----------

The CTM block can seamlessly be injected into many popular networks to generate CTM networks. CTM blocks can bring the capability of learning temporal information to 2D CNN backbone networks. Here, we adopt 2D ResNet-50 as an example, and the overall architecture of a CTM network with 2D ResNet-50 backbone is presented in Figure \[fig5\]. We add three CTM blocks (after the 1st, 3rd and 5th bottleneck block, respectively) to Res4 in the 2D ResNet-50 backbone.

Experiment
==========

Datasets
--------

Here, we evaluate the performance of CTM on the several popular datasets: Mini-Kinetics-200 [@xie2018rethinking], Kinetics-600 [@carreira2018short], UCF-101 [@soomro2012ucf101] and HMDB51 [@Kuehne11HMDB].

Mini-Kinetics-200 used in [@xie2018rethinking] is a subset of Kinetics-400 [@kay2017kinetics], which is a large-scale and popular action recognition benchmark. In Mini-Kinetics-200, 200 action classes are contained. Mini-Kinetics-200 has 80000 videos in the train set and 5000 videos in the validation set individually. [^1] Thus, we can train and test models faster and more easily on Mini-Kinetics-200, while it still provides the enough data diversity for the models training. Kinetics-600 [@carreira2018short] is a larger scale and newly released dataset and it consists of 480k videos from 600 action categories. Kinetics-600 has 390k training examples and 30k validating examples. [^2] UCF-101 [@soomro2012ucf101] includes 13320 videos divided into 101 action categories, and HMDB51 [@Kuehne11HMDB] contains around 7000 videos distributed from 51 classes.

Implementation Details
----------------------

  --------------------------------------------------------
  Model, 2D Resnet50                    Val Acc top-1 (%)
  ------------------------------------ -------------------
  baseline                                    76.76

  TCCM + shortcut (Path1 + shortcut)          78.60

  Path 2 + shortcut                           78.58

  CTM (Path1 + Path 2 + shortcut)           **78.94\
                                               **
  --------------------------------------------------------

  : Performance comparisons on Mini-Kinetics-200. We add 1 block to Res4 into 2D ResNet-50 backbone.[]{data-label="tab1"}

  ----------------------------------------
  Model, 2D Resnet50    Val Acc top-1 (%)
  -------------------- -------------------
  baseline                    76.76

  Res2                        78.87

  Res3                        78.79

  **Res4 & **78.94\
  Res5 & 78.07\
  ****
  ----------------------------------------

  : The results of adding the CTM block into different stages. One CTM block is added into different stages (Res2, Res3, Res4 and Res5) in 2D ResNet-50 backbone[]{data-label="tab2"}

[**Training**]{} In the training stage, the input of our model is a 8-frame input clip, which is sampled by a fix frame interval from 64 consecutive frames randomly clipped from the original full-length video. We use SGD algorithm with a momentum of 0.9 and a weight decay of 0.0001. For Mini-Kinetics-200, we train our model for 60 epochs and the batch size is 96 on 4 GPUs. The initial learning rate is 0.02 and decays by 0.1 at epoch 30 and epoch 50. Since Kinetics-600 is a larger dataset, the model is trained for 100 epochs. The initial learning rate is 0.02 and decays by 0.1 at epoch 50 and epoch 80. The batch-size is 128 on 8 GPUs. For UCF-101 and HMDB51, we adopt Kinetics-600 pre-trained models. We use an initial learning rate of 0.002, and the learning rate is decayed by a factor 10 every 10 epochs. The models are trained for 30 epochs. All the BN layers are fixed except the first one.

In the rest of this paper, unless specified, our backbones are pretrained on ImageNet. In order to maintaining initial behavior of backbones at the beginning of training, parameters of the last BN layers in Path 1 and Path 2 are initialized as zero to make the whole CTM block an identity mapping.

[**Inference**]{} We largely follow the inference protocol in [@wang2018non]. Top-1 accuracy on the validation set will be reported. The spatial size with $224 \times 224$ pixels is used for inference. For UCF-101 and HMDB51, we report the average accuracy across three splits.

  -------------------------------------------------
  Model, 2D Resnet50             Val Acc top-1 (%)
  ----------------------------- -------------------
  baseline                             76.76

  + 1 CTM block                        78.94

  + 3 CTM blocks                       79.13

  **+ 6 CTM blocks & **79.15\
  ****
  -------------------------------------------------

  : The results of adding 1, 3, and 6 CTM blocks into Res4 of 2D ResNet-50 backbone.[]{data-label="tab3"}

  ------------------------------------------------------------------------------------------------------
  Models                                     input                         Acc
  ----------------------------------------- ------- ------------------------------------------------- --
  TSN [@wang2016temporal] (SE-ResNeXt152)     RGB                         76.16

  StNet (SE-ResNeXt101) [@he2019stnet]        RGB                         76.04

  StNet (IRv2) [@he2019stnet]                 RGB                       **78.99\
                                                         LGD-2D [@qiu2019learning] & RGB &76.7\
                                                         LGD-3D [@qiu2019learning]& RGB & 78.3\
                                                     LGD-3D (Long) [@qiu2019learning] & RGB &**81.5\
                                                            D3D [@stroud2018d3d]& RGB &77.9\
                                                         D3D+S3D-G [@stroud2018d3d]& RGB & 79.1\
                                                          Oct-I3D [@chen2019drop] & RGB & 76.0\
                                                             **(Our) CTM & **RGB & **78.24\
                                                                       **********
  ------------------------------------------------------------------------------------------------------

  : The results on Kinetics-600. (Val Acc top-1 (%))[]{data-label="tab4"}

An Ablation Study of CTM Networks
---------------------------------

Here, we evaluate the performance of CTM on Mini-Kinetics-200 with the backbone of 2D ResNet50. In Table \[tab1\], we add a single block before the last residual block of the Res4 stage. In Table \[tab1\], the original 2D Resnet50 baseline achieves $76.76\%$ accuracy. TCCM+shortcut exhibits better performance with the accuracy of $78.60\%$. CTM obtain the best accuracy of $78.94\%$ and further improve 2D CNN Baseline’s accuracy by about $2.18\%$. This demonstrates that the action recognition result is significantly benefit from CTM blocks.

#### Which stage to add the CTM block? {#which-stage-to-add-the-ctm-block .unnumbered}

Here, a single CTM block is added into different stages (Res2, Res3, Res4 and Res5) in 2D ResNet-50 backbone, respectively. Table \[tab2\] shows the results, and the accuracies are $78.87\%$, $78.79\%$, $78.94\%$ and $78.07\%$, respectively. Note that all of the results are better than baseline, and the improvement on Res2, Res3, and Res4 is similar. For Res5, the top-1 accuracy declines, and this may be resulted from the high-level feature map in Res5 is highly abstract. In the rest of this paper, we add CTM blocks into Res4 by default.

#### How many CTM blocks? {#how-many-ctm-blocks .unnumbered}

We add one CTM block (before the last residual block), three CTM blocks (after the 1st, 3rd and 5th residual block, respectively) and six CTM blocks (after every residual block) to Res4 in 2D ResNet-50 backbone, respectively. In Table \[tab3\], CTM Network with one CTM block achieves $78.94\%$ top-1 accuracy. CTM Networks with three CTM blocks and five CTM blocks obtain the top-1 accuracy $79.13\%$ and $79.15\%$, respectively. We use CTM Network with three CTM blocks in the following experiments since they provide good performance and calculate fast.

  ----------------------------------------------------------------
  Models                                 input   UCF-101   HMDB51
  ------------------------------------- ------- --------- --------
  StNet [@he2019stnet]                    RGB     95.7       -

  STM [@jiang2019stm]                     RGB     96.2      72.2

  TSM [@lin2019tsm]                       RGB     96.0      73.6

  I3D [@carreira2017quo]                  RGB     95.6      74.8

  R(2+1)D [@tran2018closer]               RGB     96.8      74.5

  S3D-G [@xie2018rethinking]              RGB     96.8      75.9

  **(Our) CTM & RGB & **96.9& **74.6\
  ******
  ----------------------------------------------------------------

  : The results on UCF-101 and HMDB51. (Val Acc top-1 (%))[]{data-label="tab5"}

  ------------------------------------------------- ----------------- ----------------- ----------------- -------------------
  Datasets                                            Kinetics-600         UCF-101           HMDB51        Mini-Kinetics-200

  (Backbone)                                         (2D ResNet-101)   (2D ResNet-101)   (2D ResNet-101)    (2D ResNet-50)

  baseline                                                75.34             95.5              69.0               76.76

  **(Our) CTM & **78.24 &**96.9 &**74.6 &**79.13\
  **********
  ------------------------------------------------- ----------------- ----------------- ----------------- -------------------

Improvements on 2D CNN Baselines
--------------------------------

We show the improvements on 2D CNN baselines brought by CTM blocks here. For 2D ResNet-101 backbone, we add three CTM blocks (after the 6th, 12th and 18th residual block, respectively) to Res4. For UCF-101 and HMDB51, the Kinetics-600 pretrained model is used. Table \[tab-Improv\] details the accuracy improvement on different datasets. The results of our methods consistently outperform the 2D CNN baseline. It demonstrates that 2D CNN baseline significantly benefit from our CTM blocks.

Comparison with State-of-the-Arts
---------------------------------

#### Experiments on Kinetics 600 {#experiments-on-kinetics-600 .unnumbered}

Table \[tab4\] summarizes the performance of state-of-the-art methods. CTM network with 2D ResNet-101 backbone obtain the $78.24\%$ top-1 accuracy. The results demonstrate that CTM network achieves better results than most 2D methods and obtains competitive results against 3D methods.

#### Experiments on UCF-101 and HMDB51 {#experiments-on-ucf-101-and-hmdb51 .unnumbered}

In Table \[tab5\], some state-of-the-art techniques are compared on UCF-101 and HMDB51. We report the averaged results across three splits. In table \[tab5\], we can see that CTM networks achieve better results than 2D methods and competitive performance compared with 3D methods. The top-1 accuracy of our method with only RGB input achieves 96.9% on UCF-101 and 74.6% on HMDB51.

Conclusion
==========

In this paper, we proposed the Collaborative Temporal Modeling (CTM) block for action recognition. In CTM block, we proposed Temporal-Channel Convolution Module (TCCM) to build the spatial-aware temporal modeling path. Furthermore, CTM blocks can be inserted into many existing network architectures to generate CTM networks, and CTM blocks bring the capability of learning temporal information to 2D CNN networks. Experiments on several popular action recognition datasets demonstrate that CTM blocks bring the performance improvements on 2D CNN networks.

[^1]: The training/validation split files are provided on <https://github.com/s9xie/Mini-Kinetics-200>.

[^2]: We actually only downloaded about 375k videos in the train set and about 28700 videos in the validation set in Kinetics-600 due to that some videos’ youtube\_ids are no longer available. For Mini-Kinetics-200, we actually have only about 74000 videos in the train set and about 4700 videos in the validation set for the same reason.


Moody, Mad and Melancholy.
Isn't It Romantic?

By

Roger Kimball

Updated Dec. 20, 2000 12:01 am ET

"T here is almost universal agreement," W.H. Auden once remarked, that the romantic hero is "always unhappy. To be happy is almost a proof that one is not a hero." Charles Baudelaire made a similar observation about his own romantic disposition: "I do not pretend that Joy cannot associate with Beauty, but I will maintain that Joy is one of her most vulgar adornments, while Melancholy may be called her illustrious spouse."

The British writer Anita Brookner does not quote either of these passages...


Poland is my home country and I miss it dearly. Sometimes, I even think I want to move back there and try living there for a change. If I had to choose a city to live in Poland, Warszawa (Warsaw) would be my first choice. There are many reason why I love that city so much.

First of all, Warszawa is very metropolitan and this gypsy girl loves city life. My favorite thing to do in Warszawa is just walking around the Old Town admiring the architecture. Warszawa was heavily destroyed during World War II and the Old Town is what this city used to look like. I feel sad and angry when I think of the atrocities the war did to the people and the cities of Europe. So much history, art and architecture was destroyed during that time. However, on the positive side I am happy to see that Warsaw is having it's second revival. There are interesting new constructions and developments all over the city and tourism is booming. If you visit Warszawa you will most likely start with a tour of the Old town. Sure, it is very touristy but it is still a nice place to walk around and get something to eat or drink.

I always have a great time when I visit Warszawa, but this time it was particularly awesome. I am very grateful to our host Ania E. who invited us to spend a weekend with her. Through her I learned about a side of Warszawa that I had never known.

Saturday, we went to support the LGBT community and we participated in the Pride Parade. It was a great event! Unfortunately, the Polish government is getting more conservative. The rights of the LGBT community are constantly abused. I don't want to get into much politics here but come on Poland, get with the program like the rest of Western Europe.

After the parade we went to an after party in the Finnish Houses in Jazdow district. After World War II Stalin gifted the wooden houses to the Polish people as a part of post-war reparation program. These houses were given mostly to architects who were recruited to rebuild Warszawa after WW II. Over the years there has been many plans to tear down these cute little cabins due to it's prime central location. What is great about the Finnish houses is that you would never expect to see them in the city center. The quiet Jazdow district with its wooden cabins, magical gardens and alleys looks more like the Polish country side than a city. For a long time this was Warszawa's best kept secret.

After the parade our host and some of our new friends took us to a punk party in a squat. What is a squat (or polish skłot) ? Here is what I have learned. A squat is a relatively new concept happening in Warszaw. Squats are underground "social hang out places/party places". When I say underground I mean that not everybody is welcome there. If you want to party in a squat you either have to be a super cool punk ( which I am not) or you need to have super cool friends who know where the squats are and they will take you there. The squat that we went to was located in an old abandoned walk in clinic ( Przychodnia in Polish) on Skorupki street. We paid the cover charge. I think it was 10zl ( about $2.). What?!! The place inside reminded me of some slightly dodgy but cool place from the Trainspotting movie. The interior is dark with some neon light and the walls are covered in cool graffiti. The top level has a weird single bathroom and it was the only room in the squat with doors. The rest of the doors have been replaced with curtains. There are multiple rooms where people were hanging out: a pool table room, "bar stations" and a huge space where people leave their bikes. There is even a changing room because some punks live in the squat. I am not surprised that you have to know somebody to get in there. There were two bars where you could get a drink and there was a dance party in the basement. Because squats are illegal assemblies they are raided by the police all the time in order to shut them down. I think the squats are cool and you are guaranteed to have a great time if you are into that style!


Thank you for sharing your question with the EmpowHER community and welcome!

Starting birth control pills can and will change your menstrual cycle.

Since you are experiencing a period every two weeks, please call the doctor who prescribed them to you and explain what is happening. The pill is suppose to help regulate your period. You might need to try a different pill, but again, that is all up to your doctor.

Here is a great article from the EmpowHER community about why periods are irregular and why one can come every two weeks.

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Health care spending

January 7, 2013 - 9:17 PM

Medical spending increased 4.1 percent to $2.7 trillion in 2011, but the percentage of the Gross Domestic Product remained stable at 17.9 percent for the third consecutive year. Here's a look at the ups and downs of health care spending in 2011.

UP

Medicare spending: Increased 6.2% in 2011 after a 4.3% increase in 2010.

Cost of doctor and clinical services: Increased 4.3% in 2011 after a 3% increase in 2010.

Spending on prescription drugs: Increased 2.9% in 2011 after a historically low 0.4% in 2010.

Out-of-pocket spending for health care: Increased 2.8% in 2011 after a 2.1% increase in 2010.

DOWN

Medicaid spending: Increased only 2.5% in 2011 after a 5.9% jump in 2010.

Use of hospital services: Fell 4.2% from 2010.

Source: Office of the Actuary at the Centers for Medicare and Medicaid Services


In the second major police raid of the day, the RCMP have moved in on the rail blockade near New Hazelton, B.C., erected this afternoon by members of the Gitxsan Nation. The RCMP have now confirmed they arrested 14 people.

Gitxsan hereditary chiefs Norman Stevens (Spookw), Yvonne Lattie (Gwinitxw) and Larry Patsey (Dawamuukw) have been arrested, according to social media posts from inside the raid. Arresting hereditary chiefs on their own territory would represent a significant escalation in the ongoing crisis surrounding the construction of the Coastal GasLink pipeline through Wet’suwet’en territory.

“Come to the tracks and follow the RCMP to where they take the Gitxsan chiefs,” wrote a blockade organizer on Facebook. “We must not leave them.”

Members of the Gitxsan Nation and supporters shut down Highway 16, the Highway of Tears, saying they wouldn't re-open it until those arrested were released. They were released early the next morning, and the highway was reopened.

The New Hazelton blockade was originally erected in solidarity with the Wet’suwet’en hereditary chiefs during the RCMP raids on three Wet’suwet’en sites earlier this month. It was taken down last week in hopes negotiations with the Wet’suwet’en hereditary chiefs would take place, then went back up this afternoon in response to the police raid in Tyendinaga Mohawk territory and the RCMP’s failure to leave the remote Morice Forest Service Road, where the three earlier raids on the Wet'suwet'en took place.

The blockade participants' only demands were that the RCMP withdraw from Wet’suwet’en territory and the provincial and federal governments agree to negotiate.

According to reports from participants, the RCMP denied media access to the area.

The video below was shot by a participant in the Gitxsan blockade. It shows the RCMP raid and arrests on Gitxsan territory this evening. Shortly after the 14-minute mark, a woman whom participants identify as a journalist is grabbed by RCMP officers and led away. Her camera is apparently left behind on the tracks. The RCMP declined to respond immediately to questions about whether this woman identified herself as a journalist, saying they had not yet spoken to all the officers involved. They gave the same response to questions about whether they barred media from accessing the site. We'll update when we get an answer.


"I have assured the state government of extending all kind of support for the development of tribals including primitive tribal groups and strengthening of panchayati raj institutions," Deo told reporters after the meeting.

Stating that ensuring tribal rights was the objective of his Ministry, Deo said he would like to assist the state government's activities for the welfare of tribal population.

Replying to a question, Deo said he would take up the matter related to the announcement of minimum support price (MSP) for minor forest produce with the Centre.

Patnaik claimed that the state government had achieved a lot in implementing the provisions of the Forest Rights Act (FRA). About three lakh tribal families had been allotted forest land under the FRA, Patnaik said at the meeting.

The chief minister urged Deo to ensure that tribal students get study loans easily for higher education.

"We have also demanded sanction for opening of more residential schools for the tribal children, training centres for unemployed youths," a senior government official said.


Effects of tibolone on estrogen biosynthesis in the mammary tissue of postmenopausal monkeys.
To evaluate the long-term effects of tibolone on estrone sulfate (E1S)-sulfatase activity in breast tissue in a primate model (Macaca fascicularis) in comparison with conventional hormone therapies. Ovariectomized female animals (n = 112) were randomized into five groups and treated for 2 years. Treatment included tibolone at 0.05 mg/kg (LoTib, n = 23) or 0.2 mg/kg (HiTib, n = 23), conjugated equine estrogens at 0.042 mg/kg (CEE, n = 24), CEE + medroxyprogesterone acetate at 0.167 mg/kg (CEE+MPA, n = 21), or placebo (controls, n = 21). E1S-sulfatase activity was evaluated by incubating homogenized breast tissue with [H]-E1S. Thin-layer chromatography was performed to separate the products estrone (E1) and estradiol (E2). Histomorphometry was performed to measure the amount of epithelial and fat tissue in the mammary gland. Significantly more E2 than E1 was produced in all groups. E1S-sulfatase activity did not differ among the groups. E1S-sulfatase activity was highest in HiTib animals with less fatty breasts (5.9 fmol total estrogen/mg of protein/min; P < or =0.05) and lowest in HiTib animals with more fatty breasts (2.8 fmol total estrogen/mg of protein/min; P = 0.004 relative to less fatty breasts). We conclude that tibolone had a differential effect on local estrogen biosynthesis in animals with high and low breast fat content. Therefore, breast tissue composition affects the steroidogenic response to hormonal treatment.


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A combinatorial approach to the peptide feature matching problem for label-free quantification.
Label-free quantification is an important approach to identify biomarkers, as it measures the quantity change of peptides across different biological samples. One of the fundamental steps for label-free quantification is to match the peptide features that are detected in two datasets to each other. Although ad hoc software tools exist for the feature matching, the definition of a combinatorial model for this problem is still not available. A combinatorial model is proposed in this article. Each peptide feature contains a mass value and a retention time value, which are used to calculate a matching weight between a pair of features. The feature matching is to find the maximum-weighted matching between the two sets of features, after applying a to-be-computed time alignment function to all the retention time values of one set of the features. This is similar to the maximum matching problem in a bipartite graph. But we show that the requirement of time alignment makes the problem NP-hard. Practical algorithms are also provided. Experiments on real data show that the algorithm compares favorably with other existing methods. binma@uwaterloo.ca Supplementary data are available at Bioinformatics online.


Motor response investigation in individuals with cerebral palsy using near infrared spectroscopy: pilot study.
Cerebral palsy (CP) describes a group of motor impairment syndromes secondary to genetic that may be due to acquired disorders of the developing brain. In this study, near infrared spectroscopy (NIRS) is used to investigate the prefrontal cortical activation and lateralization in response to the planning and execution of motor skills in controls and individuals with CP. The prefrontal cortex, which plays a dominant role in the planning and execution of motor skill stimulus, is noninvasively imaged using a continuous wave-based NIRS system. During the study, 7 controls (4 right-handed and 3 left-handed) and 2 individuals with CP (1 right-handed and 1 left-handed) over 18 years of age performed 30 s of a ball throwing task followed by 30 s rest in a 5-block paradigm. The optical signal acquired from the NIRS system was processed to elucidate the activation and lateralization in the prefrontal region of controls and individuals with CP. The preliminary result indicated a difference in activation between the task and rest conditions in all the participant types. Bilateral dominance was observed in the prefrontal cortex of controls in response to planning and execution of motor skill tasks, while an ipsilateral dominance was observed in individuals with CP. In conjunction, similar contralateral dominance was observed during rest periods, both in controls and individuals with CP.


DICER1: mutations, microRNAs and mechanisms.
Dicer is central to microRNA-mediated silencing and several other RNA interference phenomena that are profoundly embedded in cancer gene networks. Most recently, both germline and somatic mutations in DICER1 have been identified in diverse types of cancer. Although some of the mutations clearly reduce the dosage of this key enzyme, others dictate surprisingly specific changes in select classes of small RNAs. This Review reflects on the molecular properties of the Dicer enzymes in small RNA silencing pathways, and rationalizes the newly discovered mutations on the basis of the activities and functions of its determinants.


Tag:
tunnels

Results

The team met up in the middle of town but not before Navki and Raru decided to help Fish with the warehouse. After a while, Navki returned to the Falcon to relax and tend to the boat.
While that happened, the rest of the went to the mayor and gave him …


Robert Nordén

Robert Fredrik Nordén (27 September 1926 – 29 July 1998) was a Norwegian economist, civil servant and politician for the Labour Party. He was the director of the Norwegian State Railways from 1978 to 1988.

He was born in Oslo, and was a cand.oecon. by education. He was appointed state secretary in the Ministry of Transport and Communications on 15 August 1960 as a part of the Gerhardsen's Third Cabinet. On 6 January 1961 he left office and was hired as deputy under-secretary of state, an administrative position in the department.

In 1969 he was appointed director in the Norwegian State Railways, and in 1978 he became director-general. He left in 1987, and Tore Lindholt became acting director-general. Nordén worked as an advisor in the Ministry of Petroleum and Energy from 1988 to 1992.

References

Category:1926 births
Category:1998 deaths
Category:Norwegian economists
Category:Norwegian state secretaries
Category:Labour Party (Norway) politicians
Category:Directors of government agencies of Norway
Category:Norwegian State Railways (1883–1996) people
Category:20th-century economists


Demonstration of 72-kDa and 92-kDa forms of type IV collagenase in human skin: variable expression in various blistering diseases, induction during re-epithelialization, and decrease by topical glucocorticoids.
Type IV collagenases have been shown to play an important role in tumor metastasis and wound healing. In the present study, we have demonstrated the presence of 72-kDa and 92-kDa forms of type IV collagenase in human skin by biochemical and in situ hybridization techniques. In situ hybridization allowed us to localize the 72-kDa form mostly to fibroblasts and the 92-kDa form to the epidermis and endothelial cells. The presence of type IV collagenase was confirmed by Western blotting. Enzyme activity was assayed in spontaneous blisters (18 subjects) and suction-induced blisters (29 subjects) by the zymography method, and by using type IV collagen as the substrate. Thus, it was possible to detect both the 92-kDa and 72-kDa forms in spontaneous and induced blisters. An especially high level of the 92-kDa enzyme was found in a bullous pemphigoid patient. Type IV collagenases were studied during re-epithelialization of the blister, using the suction-blister model. There was a marked induction of the 92-kDa type that was confirmed to be in the regenerating, migratory, epithelium by in situ hybridization studies. These results indicate that 92-kDa type IV collagenase may play an essential role in the normal physiology and integrity of the skin and may be an important regulator of re-epithelialization. It was also shown that potent topical glucocorticoid down-regulated the 92-kDa type collagenase, suggesting that glucocorticoids may have a beneficial role in some skin diseases by decreasing type IV collagenase activity and, thus, reducing tissue destruction.


Coccidioides immitis and Mycobacterium tuberculosis diagnosed by endoscopic ultrasound.
The use of endoscopic ultrasound in staging non-small cell lung cancer is well known. Its role in diagnosing non-malignant conditions that cause mediastinal adenopathy is still not well established. We diagnosed Coccidioides immitis and Mycobacterium tuberculosis in two patients using endoscopic ultrasound. To our knowledge this is the first case of Coccidioidomycosis to be diagnosed by endoscopic ultrasound.


Introduction
============

The locus coeruleus (LC) is a small, rod-shaped nucleus located in the pontine tegmentum (PT) along the lateral edge of the floor of the fourth ventricle. It contains noradrenergic neurons that send widespread projections throughout the central nervous system.[@b1],[@b2] LC neuronal loss is one of the characteristic pathologies of early-stage Parkinson\'s disease (PD) and Alzheimer\'s disease (AD).[@b3]--[@b8] Detection of LC degeneration by neuroimaging could therefore contribute to the early or preclinical diagnosis of AD facilitating the use of disease-modifying interventions in the future. However, conventional magnetic resonance imaging (MRI) has not been successful in delineating the LC. Recently, visualization of neuromelanin-containing nuclei, such as the LC and substantia nigra pars compacta (SNc), has become possible through the introduction of a fast spin-echo (FSE) T1-weighted image[@b9],[@b10] that can enhance T1-related contrast of neuromelanin.[@b11],[@b12] Using this method, signal attenuation or volume loss in the SNc and/or LC detected in patients with PD, various parkinsonisms and depression, results that might reflect pathological or functional changes in these nuclei.[@b9],[@b13],[@b14] However, changes in the LC of patients with AD or mild cognitive impairment (MCI) have not yet been investigated. Thus, we attempted to determine: (i) whether high-resolution FSE T1-weighted MRI can detect changes in the LC of patients with AD and MCI; and (ii) if this method can in fact predict the progression of MCI to AD.

Methods
=======

Patients
--------

MRI was prospectively carried out between 1 November 2006 and 31 December 2011 in the Department of Neurology and Gerontology, School of Medicine, Iwate Medical University, Morioka, Japan. Participants were 22 consecutive patients with AD who met the criteria for probable AD according to The National Institute of Neurological and Communicative Diseases and Stroke and Alzheimer\'s Disease and Related Disorders Association[@b15],[@b16] and Functional Assessment Staging (FAST) Stage 4 (11 men and 11 women; age 59--86 years \[median 76.5 years\]; duration of disease 0.5--10 years \[median 5 years\]), and 47 consecutive patients with amnestic MCI who fulfilled Petersen\'s criteria[@b17],[@b18] and FAST Stage 3 criteria (22 men and 25 women; age 60--87 years \[median 73 years\]; duration of disease, 0.5--7 years \[median 2 years\]). Scores on the Mini-Mental State Examination (MMSE) carried out within 1 week of the MR examination were 16--26 (median 22) in patients with AD and 19--30 (median 25) in patients with MCI. A total of 15 out of the 22 patients with AD and 25 out of the 47 patients with MCI were given cholinesterase inhibitors. Additionally, 26 healthy elderly individuals with no known neurological disorders (12 men, 14 women; age 60--83 years \[median 70 years\]) were also examined. These 26 control subjects had no abnormal neurological findings and their MR T2-weighted images were normal.

After being observed for at least 2 years (2--6 years \[median 4 years\]), 18 patients with MCI (8 men and 10 women; age 62--85 years \[median 73 years\]) met the criteria for probable AD according to the The National Institute of Neurological and Communicative Diseases and Stroke and Alzheimer\'s Disease and Related Disorders Association. These patients were then included in the AD-converted MCI (MCIc) group. A total of 20 patients who did not meet the criteria for probable AD (8 men and 12 women; age 60--87 years \[median 74 years\]) were included in the non-converted MCI (MCInc) group. The remaining nine patients with MCI were excluded from the study; one patient showed reversion of symptoms, and eight patients stopped visiting the hospital.

We carried out all examinations after obtaining both the approval of the Iwate Medical University Research Ethics Committee and after obtaining written informed consent from each participant.

Imaging protocol
----------------

Using a 3-Tesla MR scanner (Signa Excite HDxt; GE Healthcare, Milwaukee, WI, USA), we obtained oblique axial FSE T1-weighted images (repetition time 600 ms; echo time 14 ms; flip angle 90 degrees; echo train length 2; number of excitations 8; matrix size 512 × 320; field of view 220 mm; pixel size 0.42 × 0.68 mm; number of slices 10; slice thickness 2.5 mm; interslice gap 1 mm; acquisition time 12 min) as previously reported.[@b9],[@b10] Images were carefully set perpendicular to the fourth ventricle floor, with coverage between the posterior commissure and the inferior border of the pons. T1- and T2-weighted images of the entire brain were also obtained from all participants in order to exclude the possibility of other neurological disorders, and to confirm the absence of coexisting lesions, such as discrete cerebral infarcts and hemorrhages, that could interfere with further assessment.

Data processing
---------------

For quantitative evaluation of the high-resolution FSE T1-weighted image, signal intensities were measured using circular regions of interest (ROI) of 1 mm^2^ and 10 mm^2^ for the LC and PT, respectively. ROI were viewed on a liquid crystal display in the section through the upper pons (Fig. [1](#fig01){ref-type="fig"}a). One of the authors (J T), who was blind to subject information, measured the signal intensity of the LC at 7 mm below the section through the inferior edge of the inferior colliculus. Three measurements were carried out at 1-week intervals, and the obtained values were then averaged. The ROI of the LC was manually derived using the highest intensity position around the floor of the fourth ventricle. LC contrast ratios (LC-CR) were calculated using the following equation: LC-CR = (SI~LC~ − SI~PT~) / SI~PT~, where SI~LC~ is the averaged signal intensity the left and right LC, and SI~PT~ is the signal intensity of the PT.

![High-resolution fast spin-echo T1-weighted imaging of a healthy subject, patients with mild cognitive impairment (MCI) and a patient with Alzheimer\'s disease (AD). (a) A healthy subject. (b) A patient with MCI without conversion to AD (MCInc). (c) A patient with MCI converted to AD (MCIc). (d) A patient with AD. (a) In the healthy subject, a punctate region of high signal intensity showing the locus coeruleus (LC) is evident (arrow), (b--d) whereas the signal intensity is remarkably diminished in patients with MCInc, MCIc, or AD (arrows). Small circle, region of interest for measuring signal intensity in the LC (1 mm^2^); large circle, region of interest for the pontine tegmentum (10 mm^2^).](ggi0015-0334-f1){#fig01}

For evaluating mesial temporal atrophy, interuncal distance (IUD) was measured bilaterally from the section intersecting the uncus on the T1-weighted image, as previously reported.[@b19] The measurements were carried out a total of three times with 1-week intervals between measurements, after which the measured values were averaged.

Statistical analyses
--------------------

Kruskal--Wallis and Steel--Dwass tests were used to determine differences in LC-CR among the AD, MCIc, MCInc and control groups. These tests were also carried out to evaluate differences in participant demographics among the groups. To determine the sensitivity and specificity of high-resolution FSE T1-weighted imaging for discriminating among the four groups, receiver operating characteristic (ROC) analyses were carried out. Cut-off values were determined by the Youden index. Multiple regression analysis was also used to examine whether the demographics were independently related to LC-CR. Intraoperator agreement of the obtained measurements was determined by calculating the intraclass correlation coefficient (ICC). The alpha level for all analyses was 0.05.

Results
=======

MR examination was successfully carried out for all participants without significant motion artifact, and the images obtained were eligible for further quantitative analyses. Patient demographics are shown in Table [1](#tbl1){ref-type="table"}. No significant differences in sex or administration of cholinesterase inhibitors were observed among the groups. Several observations were made: the healthy subjects were younger than patients with AD (*P* = 0.042, Steel--Dwass test); disease durations were significantly longer in patients with AD than in patients with MCIc and MCInc (*P* = 0.045 and 0.008, respectively; Steel--Dwass test); MMSE scores were significantly lower in patients with AD than in patients with MCInc (*P* \< 0.0002, Steel--Dwass test); and IUDs were significantly larger in the AD group than in the control group (*P* = 0.0024, Steel--Dwass test). However, none of these demographic parameters was correlated with the LC-CR. Similarly, treatment with cholinesterase inhibitors was not correlated with the LC-CR (*P* = 0.5724, Steel-Dwass test). The correlation coefficients describing a lack of association between LC-CR and age, disease duration, MMSE, sex and cholinesterase inhibitor medication were *r* = 0.022 (*P* = 0.835), *r* = −0.049 (*P* = 0.700), *r* = −0.027 (*P* = 0.834), *r* = 0.064 (*P* = 0.0548) and *r* = 0.026 (*P* = 0.843), respectively.

######

Demographics of patients with Alzheimer\'s disease, patients with mild cognitive impairment and healthy individuals

                                                            AD                                          MCIc         MCInc        Control                                 *P-*value[†](#tf1-3){ref-type="table-fn"}
  --------------- ----------------------------------------- ------------------------------------------- ------------ ------------ --------------------------------------- -------------------------------------------
  Age             Range                                     59--86                                      62--85       60--87       60--83                                  0.027
  (years)         (median)                                  (76.5)                                      \(73\)       \(75\)       (70)[\*](#tf1-1){ref-type="table-fn"}
  Sex             Men                                       11                                          8            8            12                                      0.93
  (%)             \(50\)                                    \(44\)                                      \(40\)       \(46\)
  Duration        Range                                     0.5--10                                     0.5--7       0.5--7       NA                                      0.005
  (years)         (median)                                  (5)[\*\*](#tf1-2){ref-type="table-fn"}      \(2\)        \(2\)
  MMSE            Range                                     16--26                                      19--28       19--30       NA                                      \<0.001
  (median)        (22)[\*\*](#tf1-2){ref-type="table-fn"}   \(25\)                                      \(26\)
  CE inhibitors   Number                                    15                                          10           15           NA                                      0.37
  (%)             \(68\)                                    \(56\)                                      \(75\)
  IUD             Range                                     23.2--33.5                                  19.0--31.1   20.8--33.5   19.1--31.0                              0.004
  \(mm\)          (median)                                  (27.9)[\*\*](#tf1-2){ref-type="table-fn"}   (26.2)       (24.9)       (25.3)

*P* \< 0.05,

*P* \< 0.01 (Steel--Dwass test);

Kruskal--Wallis test.

AD, Alzheimer\'s disease; CE, cholinesterase; IUD, interuncal distance; MCIc, mild cognitive impairment converter; MCInc, mild cognitive impairment non-converter; MMSE, Mini-Mental State Examination; NA, not applicable.

LC-CR ranged from 1.80--16.09% (median 9.30%), 3.45--14.84% (median 6.87%), 3.01--19.19% (median 7.72%) and 6.24--20.94% (median 14.35%) in the AD, MCIc, MCInc, and control groups, respectively. A significant difference was observed among the groups (*P* \< 0.0001) using the Kruskal--Wallis test.

Additional analysis using the Steel--Dwass test showed that the LC-CR were markedly decreased in the AD, MCIc and MCInc groups (*P* = 0.0031, 0.0002, and 0.0168, respectively) compared with the healthy control group (Fig. [2](#fig02){ref-type="fig"}), whereas no significant differences were observed among the AD, MCIc and MCInc groups (*P* = 0.45--1.00).

![Locus coeruleus contrast ratios in healthy subjects and in patients with mild cognitive impairment (MCI) or with Alzheimer\'s disease (AD). The locus coeruleus contrast ratio was markedly decreased in the MCI without conversion to AD (MCInc), MCI converted to AD (MCIc) and AD groups than in the healthy control group, whereas no significant difference was observed between the AD, MCIc and MCInc groups.](ggi0015-0334-f2){#fig02}

ROC analyses showed that the area under the ROC curve (AUC) of the LC-CR for discriminating the AD and MCI groups from the healthy control group was 0.83, and the sensitivity and specificity were 85.0% and 69.2%, respectively, when the cut-off value was set at 12.5% (Fig. [3](#fig03){ref-type="fig"}). In contrast, the AUC for discriminating the AD and MCIc groups from the MCInc and healthy control groups was 0.69, and the sensitivity and specificity were 63.4% and 67.4%, respectively, with a cut-off value of 9.18%.

![Receiver operating characteristic curves of the locus coeruleus contrast ratio in patients with Alzheimer\'s disease, mild cognitive impairment and normal control group.](ggi0015-0334-f3){#fig03}

ICC values for the manual measurements of LC were 0.984--0.997, showing excellent intraoperator agreement.

Discussion
==========

In the present study, we successfully detected signal attenuations in the LC of patients with AD or MCI using high-resolution FSE T1-weighted imaging. This finding corresponds well to pathological findings of a decrease in neuromelanin contents as a result of neuronal loss in the LC of these patients. Thus, high-resolution FSE T1-weighted imaging could enable direct visualization of the LC degeneration, which occurs in AD and MCI, a progression that cannot be detected by other MRI techniques.

We observed that signal attenuation in the LC was evident even in patients with MCI; furthermore, there were no substantial differences in signal changes between the MCI and AD groups. Noticeably, this relative stability of the LC signal differs from what is observed in mesial temporal lobe atrophy. Atrophy of mesial temporal lobe structures, such as the hippocampus, entorhinal cortex and amygdala, tends to follow AD progression.[@b20],[@b21] In the present study, IUD, which reflects mesial temporal lobe atrophy, gradually increased along with disease severity, and showed no substantial correlation with LC changes.

Pathological changes of the LC in AD and MCI have been thoroughly investigated. AD is characterized by two pathological hallmarks: extracellular amyloid deposits[@b22],[@b23] and intraneuronal neurofibrillary tangles as a result of phosphorylated tau.[@b22] Amyloid beta deposits are first observed in the neocortex, then in the hippocampal formation, subcortical regions and finally in several brainstem nuclei.[@b22],[@b23] Hence, amyloid beta deposits in the LC are not detectable until the disease has progressed significantly. In contrast, the deposition of phosphorylated tau and subsequent neuronal loss in the LC are observed from very early stages of AD, MCI and preclinical AD.[@b3],[@b6],[@b24]--[@b27] However, no investigators have previously carried out an *in vivo* evaluation of alterations in the LC of patients with AD or MCI, mainly because the LC has been an "invisible" structure on conventional MR images. Thus, signal attenuation in the LC, observed using high-resolution FSE T1-weighted imaging, could comprise an effective marker for the detection of early or prodromal stages of AD.

Contrary to our expectations, we observed significant signal attenuation in the LC not only in the MCIc group, but also in the MCInc group, with no substantial differences being observed between these two groups. These results suggest that signal changes in the LC are not specific to the prodromal stage of AD and cannot predict whether patients with MCI will convert to AD. However, conversion to AD can be predicted by quantitative assessment of mesial temporal lobe atrophy using sophisticated voxel-based morphometry (VBM) techniques.[@b20],[@b21],[@b28]

MCI can result from a number of heterogeneous causes, such as dementia with diffuse Lewy bodies, PD dementia and argyrophilic grain dementia.[@b29] Neuronal loss in the LC was reported in several pathological studies on these disorders,[@b7],[@b30]--[@b32] consistent with the LC signal changes seen in the MCInc group in the present study. Thus, the present results suggest that neuronal loss in the LC might be one of the cardinal mechanisms of MCI. Furthermore, we could not distinguish MCI that converted to AD from MCI that did not convert to AD by using this method.

In considering alternative artifactual interpretations of these data, we can make note of several previous observations. According to a report by Kaneda, in patients who have had gadolinium, high signal intensity might be observed.[@b33] However, the present patients and participants had not used gadolinium. Manganese is a heavy metal that also produces high-intensity signals in T1-weighted images. The causes of manganese deposition in the brain are hepatic encephalopathy and intravenous hyperalimentation. In the present study, none of the participants and patients had experienced these conditions.

Conversely, iron shows low intensity on T2-weighted imaging of the brain. In contrast, iron generally produced no changes in high-resolution FSE T1-weighted imaging. For example, although the red nucleus contains a relatively large component of iron, it did not show high intensity using this sequence.

The present study had several limitations. First, we did not compare LC changes with other biomarkers for LC-related norepinephrine dysfunction, such as norepinephrine concentration in the cerebrospinal fluid. Second, the present study could not show whether LC signal changes are present in non-AD dementias. In this study, we observed patients in the MCInc group for 2--6 years, which was too short a period to determine the final clinical diagnosis of these patients. Third, this study did not elucidate advantages of this method over VBM or other sophisticated quantitative methods except by comparing LC changes with IUD, a marker classically used to reflect medial temporal lobe atrophy. In the present study, we did not obtain 3-D T1-weighted images, which are primarily used for VBM, or T1 coronal images because of a limited examination time schedule. Direct comparison between high-resolution FSE T1-weighted imaging and VBM is required to compare the diagnostic validity of these two methods.

There are several disadvantages of the technique used for T1-weighted FSE imaging in the present study. The technique is time-consuming, has relatively low spatial resolution for assessment of the LC and suffers from substantial signal heterogeneity as a result of the heterogeneous local magnetic field at 3 Tesla, which can diminish the quantitative value of these measurements. The manual measurement we carried out might also possibly explain the large variability in measures, although intraoperator agreement was high in this study. Although all patients had mild symptoms and could finish examinations without significant motion artifact, there were some effects of cardiac cycle and cerebrospinal fluid flow dynamics.[@b34] Three-dimensional imaging techniques and automatic measurements are required to improve the precision of measurements of signal alterations in the LC.[@b35] We are currently using these techniques in our ongoing studies.

The present study showed that high-resolution FSE T1-weighted imaging enables visualization of signal attenuation in the LC of patients with AD or MCI. However, it remains unknown whether this technique can be used to detect changes in patients with preclinical AD. It is also unclear whether the LC signal can track the progression of AD symptoms or predict patient outcomes. We assessed the MCI patients as converter/non-converter by evaluating only the symptoms. Therefore, longitudinal evaluations of MR images and point-by-point analyses are required to clarify these issues. High-resolution FSE T1-weighted imaging shows significant signal attenuation in the LC of patients with AD and MCI, with or without conversion to AD, indicating that neuronal loss can occur early on in these patients.

Our deepest appreciation goes to the late Dr Satoshi Takahashi, who made an inspirational effort in leading our study during his short life. This work was partly supported by a Grant-in-Aid for Strategic Medical Science Research from the Ministry of Education, Culture, Sport, Science and Technology of Japan.

Disclosure statement
====================

The authors declare no conflict of interest.


As the fourth anniversary of the Iraq war approaches, debate over the war's future is heating up among politicians in Washington. The sides of the argument mostly fall along partisan lines, with Democrats, who control both houses of Congress, sharply criticizing the Bush administration's policies. VOA's Stephanie Ho has more on the story.

Critics of the Bush administration's Iraq policy include Democratic Senator John Kerry, who was his party's presidential candidate in 2004. He blasted the White House for sending more U.S. troops to Iraq, and said he believes there is no military solution to the ongoing violence in the country.

"I get really angry," said John Kerry. "I heard about those four soldiers killed today and I say to myself, as someone who remembers going out on patrols that sort of had a huge question mark over them, what are we doing? What are these kids doing, going out there and finding an IED [improvised explosive device] the hard way?"

The four U.S. troops were killed Sunday by a roadside bomb in Baghdad.

In an effort to quell the violence, the White House in January said it is sending 21,500 additional U.S. troops to Iraq. More recently, the Bush administration announced it is sending 4,700 more troops to Iraq, mostly to serve in a support capacity.

The Bush administration's policy was praised by White House National Security Adviser Stephen Hadley. Speaking on the ABC television program This Week, he acknowledged that some Iraqis want the U.S. military presence to be, in his words, "over."

"But the point is we need to get it in a position where the Iraqis can take responsibility for security successfully," said Stephen Hadley. "Because if we do not and we do a premature withdrawal, then what we have is a situation where, if the Iraqi forces cannot handle the situation, which is the case now, we have Iraq as a safe haven for terrorists, who will destabilize the neighbors and attack us."

U.S. Defense Secretary Robert Gates appeared on the CBS television program Face the Nation to urge critics to wait and see whether the White House's latest troop surge plan is effective.

"General Petraeus, the commander out there, has said it will probably be summer before we know whether we are being successful or not," said Robert Gates. "But I would say that the Iraqis are meeting the commitments they have made to us."

Democratic congressman and prominent critic of the Iraq war, John Murtha, indicated his distrust of the Bush administration's management, in an appearance on CNN's Late Edition.

"Every time they say there is progress, it turns out there is no progress and then they have to backtrack," said Congressman Murtha. "For instance, they say everything is getting better, yet oil production, electricity production are all below pre-war levels. Incidents have increased outside Baghdad."

He said he believes something dramatic needs to be done in order for the situation to get better. He is among supporters of legislation in the House of Representatives that includes a deadline of September 2008 for U.S. troops to be withdrawn from Iraq.

Meanwhile, thousands of people demonstrated in Washington Saturday to show their opposition to the Iraq War. There were others demonstrating in support.

Protests are expected to continue in the United States and elsewhere in the world before Tuesday's four-year anniversary of the start of the war.


Dick Cheney

Former Vice President Dick Cheney is recovering from a heart transplant he received Saturday at Inova Fairfax Hospital in Falls Church, Va.

The operation makes Cheney among more than 2,300 Americans who get heart transplants every year.

Heart transplantation has come a long way since Christiaan Barnard stitched the heart of a young woman into the chest of a middle-aged man in South Africa in 1967. That transplant recipient died 18 days later. Today, recipients can expect to get a decade or more of life from their new hearts.


1. Field of the Invention This invention relates to epitope tagging, in particular, to improved epitope tags, the nucleotide sequences that encode them, methods for using the nucleotide sequences and tags, and resulting cellular and multicellular products. 2. Background Art The publications and other reference materials referred to herein to describe the background of the invention and to provide additional detail regarding its practice are hereby incorporated by reference. For convenience, the reference materials are numerically referenced and grouped in the appended bibliography.
Epitope tagging is a recombinant DNA method for introducing immunoreactive peptides into the products of cloned genes (1-7). In particular, a DNA sequence encoding a sequence of amino acids that comprises a continuous epitope is inserted into the coding sequence of a cloned gene with the result that when the gene is expressed the protein of the gene is tagged with the epitope. The protein can then be detected and/or purified by virtue of its interaction with an antibody specific to the epitope. Epitope tags are typically 5-20 amino acids in length. Nucleotide sequences encoding the epitope are produced either by cloning appropriate portions of natural genes or by synthesizing a polynucleotide that encodes the epitope.
Epitope tagging is widely used for detecting, characterizing, and purifying proteins. The technique offers several advantages over alternative methods of detecting and purifying proteins. The small size of the epitope tag, which is usually 5-20 amino acids in length, generally has no effect on the biological function of the tagged protein. This contrasts with many larger fusion protein products, in which the activity or function of the fusion protein has been affected by the longer peptide label. Epitope tagging also offers tremendous time savings over the traditional method of producing an antibody to the specific protein being studied.
Epitope tagging involves adding a unique epitope tag peptide sequence to the protein of interest by recombinant DNA techniques, creating a fusion protein. The resulting tagged protein can then be detected by and purified with an antibody specific for the epitope tag.
Epitope tagging methods have been used in a wide variety of applications, including western blot analysis, immunoprecipitation, immunofluorescence, and immunoaffinity purification of tagged proteins.
Epitope tagging was first described in 1984 by Munro and Pelham (1). A cDNA encoding the Drosophila melanogaster heat shock protein hsp70 was tagged at the 3' end of the coding sequence with a short oligonucleotide tag encoding either nine or fourteen amino acids of the peptide Substance P. After transfection of monkey COS cells, the tagged protein was detected using an anti-substance P monoclonal antibody. Since the initial report of Munro and Pelham, hundreds of investigations using epitope tagging have been reported in the scientific literature. Epitope tagging products and kits, which include various combinations of peptides, polynucleotides, and antibodies, are currently sold by a number of companies, including Boehringer-Mannheim, Indianapolis Ind.; Berkeley Antibody Company, Berkeley, Calif.; MBL International Corporation, Watertown, Mass.; Novagen, Madison Wis.; IBI, West Haven, Conn. and Life Technologies, Gaithersburg, Md.
To epitope tag a protein by conventional means, one begins with two DNA molecules: (1) a polynucleotide which is cloned in a plasmid vector and which includes a sequence of nucleotides encoding the protein as well as regulatory sequences (i.e. promoter, translations start, etc.) needed to express the protein; and (2) an oligonucleotide encoding the epitope with which the protein is to be tagged. The oligonucleotide is designed to encode, in one of its reading frames, an epitope recognized by a known antibody. One chooses a site in the polynucleotide's protein coding sequence for insertion of the oligonucleotide. The site may be at or near the 3' or the 5' end of the coding sequence, or somewhere in between the 3' and 5' ends. The insertion site for the oligonucleotide is typically a unique restriction site. The plasmid is linearized with the restriction endonuclease, and the oligonucleotide is ligated into the site. The tagged gene is then introduced into living cells. Epitope-tagged protein, which is subsequently expressed from the tagged gene, is detected and/or purified by immunochemical means.
Using conventional epitope tagging techniques, hundreds of different proteins have been epitope-tagged with numerous distinct peptides, including the ten amino acid c-myc epitope Glu Gln Lys leu Ile Ser Glu Asp Leu (SEQ ID NO: 1) derived from the human c-myc protein (8)); the nine amino acid HA-epitope Tyr pro Tyr Pro Asp Val Tyr Ala (SEQ ID NO: 2) derived from influenza virus hemagglutinin (9, 10), the eight amino acid FLAG epitope Asp Tyr Lys Asp Asp Asp Asp Lys (SEQ ID NO: 3) derived from bacteriophage T7 (Castrucci et al., 1992. J. Virology 66: 4647-4653) and the eleven amino acid epsilon-tag epitope Lys Gly Phe Ser Tyr Phe Gly Glu Asp Leu Met Pro (SEQ ID NO: 4) derived from protein kinase C epsilon (Olah et al., 1994. Anal. Biochem. 221: 94-102). Indeed, there appears to be no practical limit to the number of possible epitope tags that can exist. Essentially any peptide can be used as an immunogen to raise antibodies that will recognize that same peptide when it is present within or at the termini of a protein (11, 12).
It is common practice in molecular biology to obtain antibodies that recognize the protein product of a cloned and sequenced gene by (1) synthesizing a peptide, typically ten to twenty amino acids in length, that corresponds to a portion of the protein, (2) immunizing an animal with the peptide, and (3) using the resulting antiserum to immunodetect or immunopurify the protein in which the peptide is situated. An example of this approach can be found in Sawin (15). A particularly relevant example can be found in Sugii et al. (13). Here, 23 overlapping peptides that cover the entire amino acid sequence of bovine conglutinin were synthesized and used individually as peptide epitopes to immunize rabbits. Every serum showed cross-reactivity with the complete conglutinin protein.
A problem with conventional epitope tagging involves a limited probability of successfully tagging the protein. Despite researchers' best efforts, not every insertion into a host polynucleotide of an oligonucleotide encoding an epitope tag is achieved in a reading frame which allows expression of the intended epitope. The probability of success using a conventional method depends, in part, on how much is known about the polynucleotide before the construction is commenced. If the nucleotide sequence is known, and if, therefore, the reading frame at the target restriction site is known, then an oligonucleotide with the epitope encoded in the correct reading frame can be chosen. In this case, the probability that a given insertion event will be the desired one is one in two for the reason that the orientation of the oligonucleotide with respect to the polynucleotide cannot be controlled by the experimenter, and only one of the two orientations will serve. If, on the other hand, the reading frame at the target restriction site is not known (as is frequently the case), then the probability of success drops to one in six because the reading frame will only be correct for one site out of three. The reading frame problem could be dealt with by using three different DNA fragments, each of which encodes the epitope tag in a different reading frame (16). However, that involves production of multiple constructs to assure finding the one of interest, which is an inefficient process.
Accordingly, for known epitope tagging procedures to be effective, the added DNA must be (1) in the appropriate orientation, and (2) in the correct reading frame. There are thus two obstacles inherent in conventional epitope tagging: an orientation obstacle and a reading frame obstacle.
The reading frame obstacle can only be avoided if the reading frame around the target restriction site is known. Otherwise, three different DNA fragments, each of which encodes the epitope tag in a different reading frame must be used. In particular, if the insertion into the coding sequence is at a random or arbitrarily selected site, e.g. at a unique restriction site, then for a given epitope-encoding oligonucleotide, the maximum likelihood that it is possible to successfully epitope-tag the gene product by insertion of the oligonucleotide at that site is only one in three (due to the reading frame obstacle). The experimenter is forced to isolate multiple insertions at the target site and test them individually in order to find the one of interest. The test may be arduous. For example, if the gene of interest is to be assayed in transgenic animals, it would be necessary to make numerous transgenic constructs and examine them individually.
In summary, when the reading frame of the target restriction site is not known, the likelihood that a particular insertion will successfully tag the protein is only one in six (due to the reading frame obstacle and the orientation obstacle). In other words, in five tries out of six the experimenter will fail, and in two cases out of three the experimenter is destined to fail.


Respiratory syncytial virus infection in infants with acute leukemia: a retrospective survey of the Japanese Pediatric Leukemia/Lymphoma Study Group.
Respiratory syncytial virus (RSV) can cause life-threatening complications of lower respiratory tract infection (LRTI) in young children with malignancies, but reports remain limited. We performed a retrospective nationwide survey to clarify the current status of RSV disease among infants with hematological malignancies. Clinical course, treatment, and outcome of patients with hematological malignancies who suffered from RSV infections at the age of <24 months during anti-tumor therapy from April 2006 to March 2009 were investigated by sending a questionnaire to all member institutions of the Japanese Pediatric Leukemia/Lymphoma Study Group (JPLSG). Twelve patients with acute leukemia were identified as having experienced RSV disease. The primary diseases were acute myeloid leukemia (n = 8) and acute lymphoblastic leukemia (n = 4). RSV infection occurred pre- or during induction therapy (n = 8) and during consolidation therapy (n = 4). Eight patients developed LRTI, four of whom had severe pneumonia or acute respiratory distress syndrome; these four patients died despite receiving intensive care. In our survey, the prognosis of RSV disease in pediatric hematological malignancies was poor, and progression of LRTI in particular was associated with high mortality. In the absence of RSV-specific therapy, effective prevention and treatment strategies for severe RSV disease must be investigated.


[Clinical aspects and pathogenetic mechanisms of cognitive impairment in arterial hypertension].
Arterial hypertension may be responsible for cognitive impairment indirectly, by means of ischemic or haemorrhagic cerebral lesions. In this regard multi-infarct dementia, subcortical dementia due to "small vessel disease" and Binswanger's syndrome are the clinical pictures more commonly observed. However also in hypertensives free from cerebrovascular events, dysfunctions in memory, attention, abstract reasoning, mental flexibility and psychomotor abilities have been found. The pathogenesis of these findings is uncertain. Small cerebral asymptomatic lesions (lacunae, leukoaraiosis) could disconnect the cortical and subcortical structures in the brain; however other factors, such as global or regional reductions of cerebral blood flow or disturbances in neurotransmitters release cannot be ruled out. The effects of anti-hypertensive therapy are conflicting, some authors reporting an improvement and others a worsening of cognitive performances. In the elderly the risk linked to hypertension may be increased by several predisposing factors and therefore this condition must be considered with attention as a pathogenetic factor of senile dementia.


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---
title: Svelte 3: Rethinking reactivity
description: It's finally here
author: Rich Harris
authorURL: https://twitter.com/Rich_Harris
---

After several months of being just days away, we are over the moon to announce the stable release of Svelte 3. This is a huge release representing hundreds of hours of work by many people in the Svelte community, including invaluable feedback from beta testers who have helped shape the design every step of the way.

We think you're going to love it.

## What is Svelte?

Svelte is a component framework — like React or Vue — but with an important difference. Traditional frameworks allow you to write *declarative* state-driven code, but there's a penalty: the browser must do extra work to convert those declarative structures into DOM operations, using techniques like [virtual DOM diffing](blog/virtual-dom-is-pure-overhead) that eat into your frame budget and tax the garbage collector.

Instead, Svelte runs at *build time*, converting your components into highly efficient *imperative* code that surgically updates the DOM. As a result, you're able to write ambitious applications with excellent performance characteristics.

The first version of Svelte was all about [testing a hypothesis](blog/frameworks-without-the-framework) — that a purpose-built compiler could generate rock-solid code that delivered a great user experience. The second was a small upgrade that tidied things up a bit.

Version 3 is a significant overhaul. Our focus for the last five or six months has been on delivering an outstanding *developer* experience. It's now possible to write components with [significantly less boilerplate](blog/write-less-code) than you'll find elsewhere. Try the brand new [tutorial](tutorial) and see what we mean — if you're familiar with other frameworks we think you'll be pleasantly surprised.

To make that possible we first needed to rethink the concept at the heart of modern UI frameworks: reactivity.

'Rethinking Reactivity' from You Gotta Love Frontend Code Camp

## Moving reactivity into the language

In old Svelte, you would tell the computer that some state had changed by calling the `this.set` method:

```js
const { count } = this.get();
this.set({
	count: count + 1
});
```

That would cause the component to *react*. Speaking of which, `this.set` is almost identical to the `this.setState` method used in classical (pre-hooks) React:

```js
const { count } = this.state;
this.setState({
	count: count + 1
});
```

There are some important technical differences (as I explain in the video above, React is not reactive) but conceptually it's the same thing.

In fact, Svelte 3 is basically Sunil's fault.

That all changed with the advent of [hooks](https://reactjs.org/docs/hooks-intro.html), which handle state in a very different fashion. Many frameworks started experimenting with their own implementations of hooks, but we quickly concluded it wasn't a direction we wanted to go in. Hooks have some intriguing properties, but they also involve some unnatural code and create unnecessary work for the garbage collector. For a framework that's used in [embedded devices](https://mobile.twitter.com/sveltejs/status/1088500539640418304) as well as animation-heavy interactives, that's no good.

So we took a step back and asked ourselves what kind of API would work for us... and realised that the best API is no API at all. We can just *use the language*. Updating some `count` value — and all the things that depend on it — should be as simple as this:

```js
count += 1;
```

Since we're a compiler, we can do that by instrumenting assignments behind the scenes:

```js
count += 1; $$invalidate('count', count);
```

Importantly, we can do all this without the overhead and complexity of using proxies or accessors. It's just a variable.

## New look

Your components aren't the only thing that's getting a facelift. Svelte itself has a completely new look and feel, thanks to the amazing design work of [Achim Vedam](https://vedam.de/) who created our new logo and website, which has moved from [svelte.technology](https://svelte.technology) to [svelte.dev](https://svelte.dev).

We've also changed our tagline, from 'The magical disappearing UI framework' to 'Cybernetically enhanced web apps'. Svelte has many aspects — outstanding performance, small bundles, accessibility, built-in style encapsulation, declarative transitions, ease of use, the fact that it's a compiler, etc — that focusing on any one of them feels like an injustice to the others. 'Cybernetically enhanced' is designed to instead evoke Svelte's overarching philosophy that our tools should work as intelligent extensions of ourselves — hopefully with a retro, William Gibson-esque twist.

## Upgrading from version 2

If you're an existing Svelte 2 user, I'm afraid there is going to be some manual upgrading involved. In the coming days we'll release a migration guide and an updated version of [svelte-upgrade](https://github.com/sveltejs/svelte-upgrade) which will do the best it can to automate the process, but this *is* a significant change and not everything can be handled automatically.

We don't take this lightly: hopefully once you've experienced Svelte 3 you'll understand why we felt it was necessary to break with the past.

## Still to come

As grueling as this release has been, we're nowhere near finished. We have a ton of ideas for generating smarter, more compact code, and a long feature wish-list. [Sapper](https://sapper.svelte.dev), our Next.js-style app framework, is still in the middle of being updated to use Svelte 3. The [Svelte Native](https://svelte-native.technology/) community project, which allows you to write Android and iOS apps in Svelte, is making solid progress but deserves more complete support from core. We don't yet have the bounty of editor extensions, syntax highlighters, component kits, devtools and so on that other frameworks have, and we should fix that. We *really* want to add first-class TypeScript support.

But in the meantime we think Svelte 3 is the best way to build web apps yet. Take an hour to go through the [tutorial](tutorial) and we hope to convince you of the same. Either way, we'd love to see you in our [Discord chatroom](chat) and on [GitHub](https://github.com/sveltejs/svelte) — everyone is welcome, especially you.


Former Grateful Dead percussionist Mickey Hart takes pride in his brain. Large, anatomically realistic 3-D animations representing the inner workings of his gray and white matter have graced video screens at several science and technology conferences. These “Glass Brain” visualizations use imaging and advanced computing systems to depict in colorful detail the fiber pathways that make Hart’s brain tick. The researchers behind the project hope it will also form the basis of a new type of tool for the diagnosis and treatment of neurological disorders.

Each Glass Brain animation overlays electroencephalography (EEG) data collected in real time atop a magnetic resonance imaging (MRI) scan—in this case Hart’s—to illustrate how different brain areas communicate with each other. Special algorithms coded into software digitally reconstruct this activity within the brain. The result is a tour of the brain that captures both the timing and location of brain signals.

Hart demonstrated the Glass Brain at a computer conference in San Jose, Calif., this past March by playing a video game called NeuroDrummer on stage. The drummer is working with the Studio Bee digital animation house in San Francisco as well as the Glass Brain’s creators to develop NeuroDrummer into a tool that can determine whether teaching someone to keep a drumbeat might help improve the neural signals responsible for cognition, memory and other functions. The Glass Brain’s brain trust includes the University of California, San Francisco’s Neuroscape Lab as well as the University of California, San Diego’s Swartz Center for Computational Neuroscience, EEG maker Cognionics, Inc. and NVIDIA, a maker of extremely fast graphics processing unit (GPU) computer chips and host of the conference where Hart performed.

Hart donned a black EEG skullcap and took the stage during the closing keynote to demonstrate a prototype of NeuroDrummer. The game came across as a therapeutic, cognition-boosting version of the once-popular Guitar Hero game, creating a very different kind of jam session for Hart. He wore an Oculus Rift virtual reality headset while tapping an electronic drum pad as he appeared to travel through a trippy digital realm resembling outer space. His attempts to keep the beat were frequently interrupted by the need to multitask—in this case, zapping virtual asteroids with a pointed finger. [See video below]

The conference audience watched the action on two wall-sized video screens at the rear of the stage. The first showed what Hart was seeing in his headset while the other displayed the Glass Brain in action. The MRI scan of Hart’s brain—taken in advance of the demo—rippled with streams of color representing different frequencies of neural activity collected by the EEG headset during gameplay.

Researchers have been fitting EEG electrodes to people’s heads for 90 years to measure the electrical signals that pulse through the brain at different frequencies as brain cells communicate with one another. A fundamental feature of how our brains work, such communication is related to higher-order abilities like attention, memory and perception, says Adam Gazzaley, founding director of the Neuroscience Imaging Center at U.C.S.F. and head of both the Glass Brain and NeuroDrummer projects.

The ability to integrate EEG and MRI data in this way is relatively new, made possible in recent years by the development of more sophisticated algorithms for decoding neural activity and the use of GPUs—originally designed to render complex video game graphics—to quickly process streaming data. EEG scanners can detect the timing of brain signals in milliseconds, although they do not provide much information about where those signals originate. That plays to MRI’s strengths: these scanners are much slower to pick up neural pulses but can pinpoint the location of neural activity by registering the relatively slow changes in brain blood flow.

Hart’s interest in neuroscience is oddly fitting. As a young musician, he played a literally instrumental role ushering in the 1960s counterculture movement that challenged conventional thinking in many areas and introduced psychedelia to the mainstream. Now Hart is involved in a new movement to expand the mind, or at least our understanding of it—this time decoding the brain’s mysteries with the help of sensors, scanners and software.

This is a personal quest for Hart, whose grandmother suffered from Alzheimer’s disease. The significance of brain rhythms first became clear to Hart about 40 years ago when he drummed for his grandmother, he said following his NeuroDrummer demo. “She hadn’t spoken in years,” he said. “I played the drum, she spoke my name. That’s power. That’s medicine. When I saw that, I knew that vibrations, in this case music, reconnected those broken pathways and brought her speech, brought her cognition back, at least for the time I was drumming.”

In the lab, Gazzaley and his team are working to ensure these visualizations depict distinct brain events that are interpretable in real time. “We will determine how to best pull out these subtle signals and how consistent they are from trial to trial and across individuals,” he says.

Gazzaley has demonstrated the Glass Brain several times since the NVIDIA conference and is currently working with his team to evaluate the project’s performance thus far. Perhaps the Glass Brain’s greatest success, he says, is the way it educates audiences about the study of how the brain works.