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920-324 Communication Server (CS) Rls. 4.0 Database Administrator

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920-324 exam Dumps Source : Communication Server (CS) Rls. 4.0 Database Administrator

Test Code : 920-324
Test Name : Communication Server (CS) Rls. 4.0 Database Administrator
Vendor Name : Nortel
: 58 Real Questions

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Nortel Communication Server (CS) Rls.

Microsoft and Nortel Make Unified Communications obtainable to corporations all over | Real Questions and Pass4sure dumps

LAS VEGAS — June sixteen, 2008 — these days at NXTcomm08, Microsoft Corp. and Nortel introduced a fully hosted provider answer that can provide a unified communications event to small and midsize groups (SMBs), extending merits previously accessible best to giant businesses. The hosted solution is in response to the Nortel Communications Server 2000 (CS 2000), an internet Protocol multimedia softswitch, and the Microsoft solution for Hosted Messaging and Collaboration edition 4.5 (HMC four.5), which includes hosted models of Microsoft workplace Communications Server 2007 and Microsoft trade Server 2007. Nortel is offering the primary integration with HMC four.5 to assist control true-time communications and deliver a unified messaging journey. in addition, to support carriers simplify and speed up deployment, Nortel’s world features portfolio offers a complete suite of integration and conclusion-consumer features to comprehensive the conclusion-to-conclusion solution.

with the aid of combining the service-grade CS 2000 with HMC four.5, carrier providers can present SMBs new, effective unified communications that mix actual-time communications, corresponding to voice, with Microsoft’s collaboration capabilities, equivalent to click-to-call, click-to-convention, ignored call email messages, mobility and telephony presence. These points all work seamlessly with the Microsoft office portfolio and the Microsoft office Communicator customer to deliver a special journey to the conclusion consumer. This new solution also allows SMBs to improvement from unified communications without needing to purchase, deploy, manipulate and maintain additional community infrastructure and complex in-residence systems, enabling them to shop operational expenses and enhance productivity.

“Unified communications is a strategic purchase that, up until today, has been leveraged primarily by using the tremendous business,” said Ruchi Prasad, vp and normal supervisor of the ingenious Communications Alliance at Nortel. “This superior joint Microsoft and Nortel answer makes unified communications attainable to businesses of all sizes. For provider providers, this solution offers new profits opportunities and the potential to expand their addressable market and carry greater-price utility and repair bundles to their shoppers.”

“we are presently seeing effective interest in the hosted unified communications solution amongst their carrier company shoppers,” spoke of Steve Zimba, managing director, world Telecom enterprise, Microsoft. “The entirely hosted unified communications solution offers opportunities for accelerated income and helps service providers deliver the related work fashion to SMBs, providing the means to talk essentially anytime, anyplace and on any device.”

At NXTcomm08, Microsoft and Nortel will exhibit the hosted unified communications answer and reveal its price in improving business agility. NXTcomm08 attendees can achieve additional info by journeying the Microsoft cubicles SL5916 and SL5923 and the Nortel sales space SL3516. more information may also be found at

About Nortel

Nortel is a recognized chief in delivering communications capabilities that make the promise of company Made elementary a fact for their purchasers. Their next-generation technologies, for both carrier provider and business networks, aid multimedia and company-important applications. Nortel’s applied sciences are designed to assist eliminate nowadays’s limitations to effectivity, pace and efficiency via simplifying networks and connecting people to the tips they want, once they need it. Nortel does enterprise in more than a hundred and fifty international locations world wide. For extra counsel, discuss with Nortel on the internet at For the newest Nortel information, visit

About Microsoft

centered in 1975, Microsoft (Nasdaq “MSFT”) is the global leader in software, features and solutions that aid people and organizations recognize their full potential.

notice to editors: if you are interested in viewing more information on Microsoft, please consult with the Microsoft internet page at on Microsoft’s corporate suggestions pages. net hyperlinks, phone numbers and titles were suitable at time of book, but may additionally due to the fact have changed. For additional assistance, journalists and analysts may contact Microsoft’s speedy Response team or other appropriate contacts listed at

LG-Nortel Unveils Line of computer IP phones | Real Questions and Pass4sure dumps

the new specifications-compliant IP cellphone 8800 series with the aid of LG-Nortel, a three way partnership of LG Electronics (LGERF.PK) and Nortel (NT), offers IP telephony performance for agencies, SMBs and provider-hosted VoIP functions.

Incorporation of a large-band codec allows for the LG-Nortel IP cell 8800 collection to carry the top of the line voice nice always, the company spoke of.

The LG-Nortel IP cell 8800 collection helps commonplace protocols together with Session Initiation Protocol (SIP) and Media Gateway manage Protocol (MGCP), which ensures compatibility with a number VoIP name servers, together with Nortel verbal exchange Server (CS) 2000 and Adaptive software Engine platform, rushing deployment throughout an enterprise however distinctive call servers are installed.

fashions within the LG-Nortel IP mobilephone 8800 sequence latitude from entry-stage contraptions designed for ease of use by using personnel, to more sophisticated models that present points equivalent to multiline support, a cellphone listing screen and Bluetooth instant headset guide.

In related information, LG-Nortel introduced that the IP telephone 8800 collection has been selected via Cypress Communications, a issuer of managed telephony and hosted unified communications options.

Nortel Softswitch Achieves Euro-PacketCable Qualification; communique Server 2000 simplest qualified Softswitch in both Europe, North the united states | Real Questions and Pass4sure dumps

GHENT, Belgium--(enterprise WIRE)--Dec. 17, 2004--adding to its success in the cable market, Nortel (NYSE:NT)(TSX:NT) nowadays introduced that its communique Server (CS) 2000 has been awarded Euro-PacketCable name administration Server (CMS) qualification fame with the aid of the Euro-Cable Certification Board (ECB), a consortium consisting of 12 fundamental European cable operators. With this Euro-Certification Wave 17 milestone, Nortel turns into the best CMS supplier with cable voice over internet Protocol (VoIP) options qualified in each Europe (via ECB) and North the us (by CableLabs).

Euro-PacketCable is the set of tactics for IP-based mostly telephony over cable and builds on the North American PacketCable ordinary developed by using CableLabs. developed on the trade's particularly successful cable modem infrastructure, Euro-PacketCable networks use IP technology to enable a wide range of multimedia features, together with IP telephony, multimedia conferencing, interactive gaming, and widespread multimedia applications.

Nortel continues to gain momentum in the cable telephony market with eight new cable VoIP contracts in 2004. the vast majority of cable operators all over the world who have chosen to installation VoIP have selected Nortel's PacketCable- and now Euro-PacketCable-certified softswitch for his or her networks. Nortel's introduced cable VoIP deployments in EMEA consist of TeleCable (Spain), ish and Kabel BW (Germany) and Telenet (Belgium).

"The reliability of Nortel's cable voice over IP solutions has allowed us to generate outstanding success in the global cable market," mentioned Dan Mondor, commonplace manager, global Cable options, Nortel Networks. "we have the products, functions and operations that permit cable operators to rapidly and aggressively convey to market new integrated video, voice and information functions. Euro-PacketCable qualification is a significant milestone in Nortel's cable approach to deliver proven, interoperable voice over IP options that pressure imaginitive cable applications and at once address the primary enterprise and networking challenges European cable operators face these days."

The ECB's Euro-PacketCable initiative verifies seller compliance to interoperable interface requirements for supplying advanced, precise-time multimedia services over two-approach cable plant. ECB trying out addresses areas like Euro-PacketCable multi-dealer interoperability, feature operation, device balance and protocol compliance. EBC trying out is carried out by tComLabs, which is a testing, consultancy, and practising business located in Ghent, Belgium.

Nortel continues to execute on its three commitments to the cable provider provider market - new product building geared particularly to cable applications; commitment to the know-how and requirements groups that serve the cable trade; and implementation actual-world solutions that address the fundamental enterprise and networking challenges of cable operators. Nortel's CS 2000 achieved PacketCable CMS 1.0 qualification in July, 2003 and PacketCable MGC 1.0 Qualification in April, 2004.

About Nortel

Nortel is a identified leader in delivering communications capabilities that raise the human experience, ignite and energy international commerce, and cozy and offer protection to the world's most important suggestions. Serving both service provider and business consumers, Nortel gives you ingenious know-how solutions encompassing end-to-conclusion broadband, Voice over IP, multimedia features and purposes, and instant broadband designed to support people remedy the world's premiere challenges. Nortel does enterprise in more than a hundred and fifty countries. For extra advice, talk over with Nortel on the web at For the newest Nortel news, seek advice from

certain tips covered during this press unencumber is ahead-looking and is field to essential dangers and uncertainties. The results or activities anticipated in these statements may additionally range materially from actual consequences or pursuits.

factors which could trigger results or events to differ from current expectations include, amongst other things: the result of Nortel's impartial evaluation and deliberate restatement or revisions of its previously introduced or filed economic outcomes; the decision of the accounting considerations announced on November eleven, 2004, including the influence of discussions with the us Securities and change fee (SEC); the have an effect on of the administration adjustments announced on April 28, 2004 and August 19, 2004; the have an impact on of the inability to meet Nortel's filing duties on guide amenities and public debt obligations; any abilities delisting or suspension of the business's or NNL's securities; the adversarial decision of litigation, investigations, highbrow property disputes and similar concerns; the sufficiency of Nortel's restructuring actions, including the work plan introduced on August 19, 2004 as up to date on September 30, 2004, including the talents for bigger exact costs to be incurred in connection with restructuring actions compared to the estimated charges of such movements; cautious or reduced spending by Nortel's clients; fluctuations in Nortel's working results and regular industry, economic and market conditions and boom costs; the communique with the aid of Nortel's auditors of the existence of fabric weaknesses in interior controls; Nortel's capability to recruit and continue qualified personnel; fluctuations in Nortel's money move, degree of spectacular debt and existing debt rankings; using money collateral to support Nortel's normal route company activities; the dependence on Nortel's subsidiaries for funding; the affect of Nortel's described advantage plans and deferred tax belongings on results of operations and Nortel's cash flows; Nortel's dependence on new product development and its means to predict market demand for particular products; Nortel's capacity to combine the operations and applied sciences of got businesses in a superb manner; the influence of swift technological and market exchange; the have an effect on of expense and product competitors; barriers to foreign boom and world economic conditions, above all in emerging markets and together with hobby rate and forex trade price fluctuations; the have an effect on of explanation within the telecommunications trade; adjustments in law of the internet; the impact of the credit dangers of Nortel's purchasers and the influence of consumer financing and commitments; inventory market volatility frequently and as a result of acceleration of the contract date or early settlement of Nortel's buy contracts; the affect of Nortel's give and outsourcing contracts that contain start and installation provisions, which, if now not met, might outcomes in the charge of colossal penalties or liquidated damages; and the long run success of Nortel's strategic alliances.

For more information with admire to certain of these and other components, see the most recent kind 10-Q/A and form 10-k/A filed with the aid of Nortel with the SEC. unless in any other case required by applicable securities legal guidelines, Nortel disclaims any intention or responsibility to replace or revise any forward-looking statements, even if because of new suggestions, future activities or in any other case.

Nortel, the Nortel brand and the Globemark are logos of Nortel Networks. PacketCable and CableLabs are logos of CableLabs.

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Communication Server (CS) Rls. 4.0 Database Administrator

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A web server for comparative analysis of single-cell RNA-seq data | real questions and Pass4sure dumps

Data collection and preprocessing

We selected a mouse gene set of interest based on the NCBI Consensus CDS (CCDS) which contains 20,499 distinct genes. For genes with multiple isoforms, they consolidate all available coding regions (Supporting Methods). To search for scRNA-seq datasets, they queried the NCBI Gene Expression Omnibus (GEO) and the ArrayExpress database for mouse single-cell RNA-seq series (See Supporting Methods for the queries they used). They then download metadata for each series returned in this query and parse this metadata to identify the distinct samples that comprise each series. They examined the metadata for each sample (e.g., library strategy, library source, data processing) and exclude any samples that do not contain scRNA-seq data.

We next attempted to download each study’s raw RNA-seq reads and for those studies for which these data are available, they developed a pipeline that uniformly processed scRNA-seq data. They use the reference mouse genome from the UCSC genome browser17 (build mm10), and align RNA-seq reads with HISAT218 version 2.1.0. They align reads as single-end or paired-end as appropriate, and discard samples for which fewer than 40% of reads align to coding regions. They represent gene expression using RPKM. See Supplementary Fig. 3 for a histogram of read counts per series.

Labeling using Cell Ontology terms

We use the Cell Ontology (CL)19 available from to identify the specific cell types which are represented in their GEO query results. They parsed the ontology terms into a directed acyclic graph structure, adding edges between terms for “is_a” and “part_of” relationships. Note that this choice of edge direction means that all edges point toward the root nodes in the ontology.

We use the name and any available synonyms for each ontology term to automatically identify the matching terms for each sample of interest (Supporting Methods). This produces a set of ontology term hits for each sample. They filter these ontology term hits by excluding any terms that are descendants of any other selected terms (e.g., term CL:0000000 “cell” matches many studies), producing a set of “specific” ontology terms for each sample—for any two nodes u and v in such a set, neither u nor v is a descendant of the other in the ontology.

Dimensionality reduction

Most current analysis methods for scRNA-seq data use some form of dimensionality reduction to visualize and analyze the data, most notably PCA and similar methods20,21 and t-SNE22. Past work has shown that while such methods are useful, supervised methods for dimensionality reduction may improve the ability to accurately represent different cell types10.

Using neural networks for dimensionality reduction has been shown to work well as a supervised technique to learn compact, discriminative representations of data23. The original, unreduced dimensions form the input layer to a neural network, where each dimension is an input unit. After training the model towards a particular objective (such as classification), the last hidden layer, which is typically much smaller in the number of units than the input layer, may be taken as a reduced dimensionality representation of the data. These learned features are referred to as neural embeddings in the literature, and here they tested a number of different neural network architectures which either explicitly optimize these neural embeddings (for example, siamese24 and triplet networks12) or those that only optimize the label accuracy. All neural networks they used were implemented in Python using the Keras API25.

Neural network architectures

Prior work showed that sparsely connected NN architectures based on protein interaction data can be more effective in determining cell types when compared to dense networks10. Here they further studied other NN networks architectures and compared their performance to the PPI and dense networks. First, they looked at another method to group genes based on the Gene Ontology (GO)26. To construct a hierarchical neural network architecture that mirrors the structure of GO, they associate input genes with GO nodes. Multiple genes are associated (and connected to) the same node. They use this grouping of the input genes as the first hidden layer of a neural network. Nodes in the next hidden layer will be constructed from GO nodes that are descendants of nodes in the prior layer. They continue this process until the last hidden layer has the desired number of nodes (the size of their reduced dimension). The final result is the network depicted in Supplementary Fig. 13. See also Supporting Methods.

Siamese architectures trained with contrastive loss

The NNs discussed above indirectly optimize the neural embedding layer by optimizing a classification target function (correct assignment of scRNA-seq data to cell types). A number of NN architectures have been proposed to explicitly optimize the embedding itself. For example, siamese neural networks11,24 (Supplementary Fig. 10) consist of two identical twin subnetworks which share the same weights. The outputs of both subnetworks are connected to a conjoined layer (sometimes referred to as the distance layer) which directly calculates a distance between the embeddings in the last layers of the twin networks. The input to a siamese network is a pair of data points and the output which is optimized is whether they are similar (same cell type) or not. The loss is computed on the output of the distance layer, and heavily penalizes large distances between items from the same class, while at the same time penalizing small distances between items from different class. Specifically, the network optimizes the following loss function:

$${\mathrm{Contrastive}}\,{\mathrm{loss}} = \mathop {\sum}\limits_{i = 1}^P \left( {Y^i} \right)L_s\left( {D^i} \right) + \left( {1 - Y^i} \right)L_d\left( {D^i} \right)$$


$$\begin{array}{l}{\mathrm{Where:}}\\ \quad \quad \,\,{P}\,{\mathrm{is}}\,{\mathrm{the}}\,{\mathrm{set}}\,{\mathrm{of}}\,{\mathrm{all}}\,{\mathrm{training}}\,{\mathrm{examples}}\,\left({{\mathrm{pairs}}\,{\mathrm{of}}\,{\mathrm{data}}\,{\mathrm{points}}} \right)\\ \quad \quad \,\,{Y}\,{\mathrm{is}}\,{\mathrm{the}}\,{\mathrm{corresponding}}\,{\mathrm{label}}\,{\mathrm{for}}\,{\mathrm{each}}\,{\mathrm{pair}}\,\left( {{\mathrm{1}}\,{\mathrm{indicates}}\,{\mathrm{that}}} \right.\\ \quad \quad \,\,\,\,{\mathrm{the}}\,{\mathrm{pair}}\,{\mathrm{belong}}\,{\mathrm{to}}\,{\mathrm{the}}\,{\mathrm{same}}\,{\mathrm{class}},{\mathrm{0}}\,{\mathrm{indicates}}\,{\mathrm{that}}\,{\mathrm{each}}\\ \quad \quad \, \, \left. {\, \, {\mathrm{sample}}\, {\mathrm{in}}\, {\mathrm{the}}\, {\mathrm{pair}}\, {\mathrm{come}}\, {\mathrm{from}}\, {\mathrm{different}}\, {\mathrm{classes}}} \right)\\ \quad \quad \, \, {D}\, {\mathrm{is}}\, {\mathrm{the}}\, {\mathrm{Euclidean}}\,{\mathrm{distance}}\, {\mathrm{between}}\, {\mathrm{the}}\, {\mathrm{points}}\, {\mathrm{in}}\,{\mathrm{the}}\,{\mathrm{pair}}\\ \quad \quad \, \, \, \, {\mathrm{computed}}\,{\mathrm{by}}\, {\mathrm{the}}\, {\mathrm{network}}\end{array}$$

$${L}_{s}\left( {D} \right) = \frac{1}{2}(D)^2$$


$$\begin{array}{l}{L}_{d}\left( {D} \right) = \frac{1}{2}\left( {{\mathrm{max}}\{ 0,m - D\} } \right)^2\\ \quad \quad \, \, {\mathrm{Where}}\,m\,\mathrm{is}\,\mathrm{a}\,\mathrm{margin}\,\mathrm{hyperparameter,}\,\mathrm{usually}\,\mathrm{set}\,\mathrm{to}\,1\end{array}$$


Following the same motivations as siamese networks, triplet networks also seek to learn an optimal embedding but do so by looking at three samples at a time instead of just two as in a siamese network. The triplet loss used by Schroff et al.12 considers a point (anchor), a second point of the same class as the anchor (positive), and a third point of a different class (negative). See Supporting Methods for details.

Training and testing of neural embedding models

We conduct supervised training of their neural embedding models using stochastic gradient descent. Although their processed dataset contains many cells, each with a set of labels, they train on a subset of “high confidence” cells to account for any label noise that may have occurred in their automatic term matching process. This is done by only keeping terms that have at least 75 cells mapping to them, and then only keeping cells with a single mapping term. This led to a training set of 21,704 cells from the data they processed ourselves (36,473 cells when combined with author-processed data). They experimented with tanh, sigmoid, and ReLU activations, and found that tanh performed the best. ReLU activation is useful for helping deeper networks converge by preventing the vanishing gradient problem, but here their networks only have a few hidden layers, so the advantage of ReLU is less clear. They also experimented with different learning rates, momentums, and input normalizations (see web server for full results).

Since their goal is to optimize a discriminative embedding, they test the quality of their neural embeddings with retrieval testing, which is similar to the task of cell-type inference. In retrieval testing, they query a cell (represented by the neural embedding of its gene expression vector) against a large database of other cells (which are also represented by their embeddings) to find the query’s nearest neighbors in the database.

Accounting for batch effects is a central issue in studies which integrate data from many different studies and experimental labs27,28. Here, they adopt a careful training and evaluation strategy in order to account for batch effects. They separate the studies for each cell type when training and testing so that the test set is completely independent of the training set. They find all cell types which come from more than one study, and hold out a complete study for each such cell type to be a part of the test set (sometimes referred to as the “query” set in the context of information retrieval). Cell types that do not exist in more than one study are all kept in the training set. For their integrated dataset, their training set contained 45 cell types, while their query set was a subset of 26 of the training cell types. After training the model using the training set, the training set can then be used as the database in retrieval testing.

Evaluation of classification and embeddings

In both training and evaluation of their neural embedding models, they are constantly faced with the question of how similar two cell types are. A rigid (binary) distinction between cell types is not appropriate since “neuron”, “hippocampus”, and “brain” are all related cell types, and a model that groups these cell types together should not be penalized as much as a model that groups completely unrelated cell types together. They have thus extended the NN learning and evaluation methods to incorporate cell type similarity when learning and testing the models. See Supporting Methods for details on how these are used and how they are obtained.

Differential expression for cell types

We use the automated scRNA-seq annotations they recovered to identify a set of differentially expressed genes for each cell type. Unlike prior methods that often compare two specific scRNA-seq datasets, or use data from a single lab, their integrated approach allows for a much more powerful analysis. Specifically, they can both focus on genes that are present in multiple datasets (and so do not represent specific data generation biases) and those that are unique in the context of the ontology graph (i.e., for two brain related types, find genes that distinguish them rather than just distinguishing brain vs. all others).

Our strategy, presented in Supplementary Algorithm 1, is DE-method agnostic, meaning that they can utilize any of the various DE tools that exist. In practice they have used Single-Cell Differential Expression (SCDE) here29. For this, they used read counts rather than RPKM, as SCDE requires count data as input. This method builds an error model for each cell in the data, where the model is a mixture between a negative binomial and a Poisson (for dropout events) distribution, and then uses these error models to identify differentially expressed genes. They also tried another method, limma with the voom transformation30, but the list of DE genes returned was too long for meaningful analysis (many of the reported DE genes had the same p-value). Results of their comparison of SCDE and limma-voom are shown in Supplementary Table 4.

Another key aspect of their strategy is the use of meta-analysis of multiple DE experiments. The algorithm attempts to make the best use of the integrated dataset by doing a separate DE experiment for each study that contains cells of a particular cell type, and then combines these results into a final list of DE genes for the cell type. See Supporting Methods for the details of this meta-analysis.

Large-scale query and retrieval

To enable users to compare new scRNA-seq data to the public data they have processed, and to determine the composition of cell types in such samples, they developed a web application. Users download a software package available on the website to process SRA/FASTQ files. The software implements a pipeline that generates RPKM values for the list of genes used in their database and can work on a PC or a server (Supporting Methods).

Once the user processes their data, the data are uploaded to the server and compared to all studies stored in the database. For this, they first use the NN to reduce the dimensions of each of the input profiles and then use approximate nearest neighbor approaches to match these to the data they have pre-processed as they discuss below.

Since the number of unique scRNA-seq expression vectors they store is large, an exact solution obtained by a linear scan of the dataset for the nearest neighbor cell types would be too slow. To enable efficient searches, they benchmarked three approximate nearest neighbor libraries: NMSLib31, ANNoY (, and FALCONN32. Benchmarking revealed that NMSLib was the fastest method (Supplementary Table 3). NMSLib supports optimized implementations for cosine similarity and L2-distance based nearest neighbor retrieval. The indexing involves creation of hierarchical layers of proximity graphs. Hyperparameters for index building and query runtime were tuned to trade-off a high accuracy with reduced retrieval time. For NMSLib, these were: M = 10, efConstruction = 500, efSearch = 100, space = “cosinesimil”, method = “hnsw”, data_type = nmslib.DataType.DENSE_VECTOR, dtype = nmslib.DistType.FLOAT. Time taken to create the index: 2.6830639410000003 secs. Hyperparameters tuned for the ANNOY library were: number of trees = 50, search_k_var = 3000. Time taken to create the index: 1.3495307050000065 secs. For FALCONN, a routine to compute and set the hyperparameters at optimal values was used. This calibrates K (number of hash functions) and last_cp_dimensions. Time taken to create the index: 0.12065599400011706 s.

Visualizing query results

We use the approximate nearest neighbors results to compute a similarity measure of each query cell to each ontology term. This is done by identifying the 100 nearest neighbors for each cell and determining the fraction of these matches that belong to a specific cell type. This generates a matrix of similarity measure entries for all query cells against all cell types which is presented as a hierarchical clustering heatmap (Fig. 5a). All visualizations are based on this matrix.

For each query cell qi and nearest neighbor nk, they calculate the similarity score as:

$$s_{n_k}^{q_i} = 1{\mathrm{/}}\left( {1 + D\left( {q_i,n_k} \right)} \right)$$

Where k ∈ [1, 100] and D is the euclidean distance function.

We sum (over the nearest neighbors) the similarities to a specific cell type ct to obtain a cumulative similarity score of the query to that cell-type:

$$S_{ct}^{q_i} = \mathop {\sum}\limits_k \left( {s_{n_k}^{q_i} \ast 1_{ct}\left( {n_k} \right)} \right)$$


$$1_{ct}\left( {n_k} \right) = \left\{ {\begin{array}{*{20}{l}} 1 \hfill & {{\mathrm{if}}\,n_k\,{\mathrm{is}}\,{\mathrm{cell}}\,{\mathrm{type}}\,ct} \hfill \\ 0 \hfill & {{\mathrm{otherwise}}}\hfill \end{array}} \right.$$

Thus, they obtain for each query a vector of similarity scores against all cell types. Finally, they normalize the vector such that the cell-type-specific cumulative similarities sum to 1. Each normalized vector forms a row in the hierarchical heatmap.

We also perform further dimensionality reduction of the query via PCA to obtain a 2D nearest-neighbor style visualization against all cell types in the database and generate the ontology subgraph that matches the input cells. Users can click on any of the nodes in that graph to view the cell type associated with it, DE genes related to this cell type, and their expression in the query cells.

In addition to matching cells based on the NN reduced values, they also provide users with the list of experiments in their database that contain cells that are most similar to a subset of uploaded cells the user selects. This provides another layer of analysis beyond the automated (though limited) ontology matching that is based on the cell types extracted for the nearest neighbors.

Finally, users can obtain summary information about cell-type distribution in their uploaded cells and can find the set of cells matched to any of the cell types in their database.

Code availability

Code for their preprocessing (alignment/quantification) pipeline is available at Code for training and evaluation of their neural network models are available at Code for their differential expression analysis to find cell-type-specific genes is available at

Sudden Windows 10 licence downgrades to forced Xcode upgrades: The week at Microsoft | real questions and Pass4sure dumps

Roundup Aside from the hoo-ha around Windows 10 licences suddenly being downgraded amid an Insider build update, last week brought Row-Level Security to Azure warehouses, Alexa's tanks rolled onto Cortana's lawn and Microsoft prepared to drop support for old versions of Xcode.

Row-Level Security in Azure SQL Data Warehouse

DBAs might have been forgiven for thinking this was already a thing. After all, SQL Server 2016 had already introduced Row-Level Security (RLS) to govern which users have access to which data without the client application being any the wiser. Azure's own Azure SQL Database had also gained the feature in 2015.

Worry no more, warehouse fans, RLS is now generally available for Azure SQL Data Warehouse, meaning that it is now no longer necessary to manually filter out rows via views or, worse, rules in the application logic. Assuming you've got your SQL or Azure Active Directory roles set up in a logical fashion and not just stuffed every user into an admin group to deal with a mystery rights bug.

Microsoft reckons the functionality will be useful in scenarios such as a healthcare provider allowing nurses to only see the records of their patients, or a multi-tenant application enforcing logical separation of data.

Alas, only filter predicates are supported – so the functionality will only filter SELECT, UPDATE and DELETE operations to exclude rows that do not satisfy the predicate. Block predicates "will be released soon", according to Microsoft, limiting the utility of the functionality somewhat.

Alexa creeps into the Windows 10 store

Already on some PCs from Lenovo, Amazon's vaguely sinister personal assistant became available to all Windows 10 users this week, further ramping up the pressure on Microsoft's homegrown helper, Cortana.

Cortana was already reeling a little from the loss of the VP responsible for the unloved technology and now faces competition from a standalone Alexa app.

Admittedly, the level of Windows 10 integration is far deeper for Cortana. However, for PCs with "compatible hands-free" access, Alexa can be woken with a keyword and will then behave just as if your PC had turned into, er, an Echo.

For users not lucky enough to have compatible hardware, a click of a button in the Alexa app is sufficient to start the Amazon assistant listening.

The majority of Alexa's skills and features are supported, giving access to smart home devices as well as the shopping list that the retailer would dearly love you to use. PC-specific functions are, alas, not supported, nor are video, communications or Spotify. Amazon does, however, say that functions aimed at PC users will added in 2019.

The long-awaited integration between Cortana and Alexa finally became available in August. That will become less relevant as Amazon extends its tentacles further into the Windows world. At least as far as UK, US and German users are concerned.

Crazy about Kaizala

Almost lost in the ongoing furore around Microsoft's Skype messaging platform, the software giant's WhatsApp-alike Kaizala quietly announced it was getting ready to go global.

The app allows users to sign in with just a phone number (sound familiar?) and can scale to groups of up to one million users. With Office 365, users can set up surveys, polls and workflows and assess the results with the likes of Power BI.

The platform was kicked off back in 2016 as a garage project before hitting preview in 2017's Future Decoded event in India. It's now available in 28 markets as part of Office 365 or standalone (or via the Google and Apple app stores) but you'll need the Pro version to do anything useful such as creating public groups.

The Pro version is due to start being rolled out worldwide "over time", giving Microsoft users yet another communications platform to add to the likes of Skype and Teams.

Elderly Xcode banished from Visual Studio App Center

Microsoft is yanking support for older flavours of Xcode 8 from its continuous integration framework, Visual Studio App Center. Xcode 8 first made an appearance back in 2016 and Microsoft is allowing version 8.3.3 to linger past the announced 28 November cut-off date.

Since the platform supports Xcode 10 (and Microsoft made quite the fuss about launch-day support for iOS 12) Redmond reckons it is time for the less-than-1-per-cent of the users of its pipeline still clinging to the old days to make the move.

And if programmers don't take the hint, kind old Microsoft will automatically bump the version to 8.3.3 for them. ®

Sponsored: Becoming a Pragmatic Security Leader

A Linux switcher doesn't miss Apple or Microsoft | real questions and Pass4sure dumps

Linux has much to offer any user, including journalists. One writer at Medium switched to Linux and said goodbye to Apple and Microsoft. He's never been happier with his computer and shared his thoughts in a long and detailed post.

On a spring day in 2012, I shut down my MacBook Air for the last time. From then on, my primary computing environment  --  at least on a laptop computer  --  was GNU/Linux. I was abandoning, as much as possible, the proprietary, control-freakish environments that Apple and Microsoft have increasingly foisted on users of personal computers.

Almost four years later, here I am, writing this piece on a laptop computer running the Linux* operating system and LibreOffice Writer, not on a Mac or Windows machine using Microsoft Word. All is well.

No, better than that  --  everything's terrific.

I'd recommend this move to lots of folks  --  not everyone, by any means, but to anyone who isn't afraid to ask some occasional questions, and especially anyone who gives some thought to the trajectory of technology and communications in the 21st Century. Most of all, to people who care about freedom.

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Dan's readers at Medium responded with their own thoughts in the comments below his article:

Steve Rummel: "Linux is my desktop of choice when executing work for my clients  --  I have more powerful tools that are easier to obtain and maintain and are of equal or greater quality with lower licensing restrictions than their non-free counterparts. R, MySql, Python, Ruby, Apache, and the general ability of Linux to support older hardware makes my work life extremely pleasant. The available GUI layers (Gnome or Cinnamon) have come a long way over the last decade, while the big players have done little to make things better in terms of the end user interface."

David Blythin: "A lot of people get hung up on perceived limitations of native applications. For example GIMP isn't an exact replacement for Photoshop, nor is it intended to be, but I've yet to find anything I can't do in GIMP that I actually want to do. Sure - PS proponents can come up with a list of stuff that GIMP can't do, but none of them are fatal errors as far as I'm concerned."

Andrew Nikolic: "So who is Linux really for? To me the hassles don't stack up against the “advantages".

I can see the argument that Apple's walled garden environment does not suit some peoples needs. However a balanced comparison should be inclusive of OS X, which is not restricted to the App Store and is traditionally a competitor of desktop operating systems like Windows and Linux. The iPad Pro and iOS angle seems out of place in this opinion piece.

Also, while data privacy concerns in Windows is worth mentioning, it's also overplayed. The reality is people should be more concerned about online data breaches, malware and management of online accounts. You don't have to look hard to find out how many consumer records are breached each year  --  it's staggering. But these breaches are the precursors to cyber-fraud and what people should be diligent about. Linux is not the answer here.

I'm not sold on the advantages of the “freedom" Linux provides, other than using it as a platform to learn and tinker."

Joe: "You're writing about freedom. Linux always runs on Windows Hardware. Have you ever revised the specifications of future Microsoft Hardware? For instance TPM/UEFI/Secureboot? Why does Purism still sell Hardware with TPM?

Freedom for me is however not based on any signing by someone allowing me to boot OS I'd like to boot.

Sorry, mate. I think your so called freedom is an illusion."

Scott Wilson: "Between Microsoft trying to force everyone to Windows 10 and their creepy spying, to Apple topping the most vulnerable operating systems list with both OSX and iOS two years in a row, the only choice left is Linux. Chrome OS (linux) if you have simple needs, or a good distro if need to do more complicated things. I love Fedora, but Ubuntu, Arch, CentOS, RHEL, SuSe, they are all pretty great now."

Abiatha Swelter: "I've been using Mint on a laptop for a couple of years now and have been pretty happy with it. The main sticking point with getting off Mac entirely is Adobe CS; there's no good Linux equivalent. My laptop is a Mythlogic/Clevo unit that I was able to get supplied with no OS, which was a nice benefit. It's a solid unit, if a bit heavy by comparison with macbooks."

Spooky Action: "Cool article, welcome to the party. I've been experimenting with and using Linux on the desktop since 1998. MkLinux running on a Powermac 8100 was the first Linux distro I cut my teeth on before discovering Debian in 1999, which I still use now. Linux has come a long way and it's awesome to see people switching to open source alternatives. BSD too! Also, if your not familiar with it, check out the Wine project. You'd be surprised how many Windows apps run on Linux now. :D"

Walton Pantland: "Good piece, but moving to Linux is way simpler than he makes it appear. I am not at all technical, but I switched 10 years ago, and it transformed my computing experience. Learning to type “sudo apt-get update" is not at all difficult, and helps you learn how your computer works.

I run Debian, and it's as straightforward as proprietary systems, even for a non-technical user like me."

Luis Anaya: "I have used Linux for many years, being the first time when I downloaded the Slakware floppies to install in a i486 system to play at work. Although I had a Linux system available throughout the years, usually as a spare system at home or work, I never ventured for it to be my desktop system 100% of the time until this year in which I now use a Xeon workstation running Debian Jessie. The reason can be summarized as follows: Windows 10 was the stroke that broke the camel's back because I just do not care for my computer to rat out on me to the manufacturer.

Thankfully, there were very few applications that I really needed on Windows, but those that I could not find a suitable Linux native substitute I manage to use them through the means of a Virtual Box running XP or through the use of Wine being that programs like Play on Linux makes it very convenient to use Wine.

Nowadays there are very few Windows exclusive programs that I use being that most of the work I do is developing a Linux software, so running all the tools for compiling and troubleshooting their system on a single platform makes it very handy."

Rhy O'drinnan: "Great article! I have two thoughts: I use GTK record my desktop constantly. What is it lacking that camtasia has? Secondly, DUDE. Try Linux Mint. It's just so much nicer than Ubuntu all the way around. I especially like the Mate version and Mate menu, and personally favor the debian edition, as it's a rolling release, and I'm a lazy server admin. The latest Mint comes with Compiz working out of the box and really pushes graphics better by default than any other OS I've used, with the possible exception of Android. Speaking of Android, I buy devices that can be formatted with Cyanogenmod and get rid of all google stuff, and only use the excellent open source market F Droid. It has most of what I need (mail, browser, music players, solitaire, etc..). Good luck to you sir!"

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