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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 title : Communication Server (CS) Rls. 4.0 Database Administrator
Vendor title : Nortel
: 58 real Questions

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Nortel Communication Server (CS) Rls.

Microsoft and Nortel develop Unified Communications obtainable to corporations entire over | real Questions and Pass4sure dumps

LAS VEGAS — June sixteen, 2008 — these days at NXTcomm08, Microsoft Corp. and Nortel introduced a fully hosted provider reply that can provide a unified communications event to minute 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 back 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, efficient unified communications that blend actual-time communications, corresponding to voice, with Microsoft’s collaboration capabilities, equivalent to click-to-call, click-to-convention, ignored summon email messages, mobility and telephony presence. These points entire 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 moreover allows SMBs to improvement from unified communications without needing to purchase, deploy, exploit and maintain additional community infrastructure and intricate 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 typical supervisor of the ingenious Communications Alliance at Nortel. “This superior joint Microsoft and Nortel reply makes unified communications attainable to businesses of entire 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 efficient 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 mode 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 reply and expose its expense in improving traffic agility. NXTcomm08 attendees can achieve additional info by journeying the Microsoft cubicles SL5916 and SL5923 and the Nortel sales space SL3516. more information may moreover live found at

About Nortel

Nortel is a recognized chief in delivering communications capabilities that develop the pledge of company Made elementary a fact for their purchasers. Their next-generation technologies, for both carrier provider and traffic networks, aid multimedia and company-important applications. Nortel’s applied sciences are designed to assist eradicate nowadays’s limitations to effectivity, pace and efficiency via simplifying networks and connecting people to the tips they want, once they necessity it. Nortel does enterprise in more than a hundred and fifty international locations world wide. For extra counsel, argue 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 complete 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 possess changed. For additional assistance, journalists and analysts may contact Microsoft’s speedy Response team or other usurp contacts listed at

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

the new specifications-compliant IP cellphone 8800 sequence with the aid of LG-Nortel, a three route 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 title servers, together with Nortel verbal exchange Server (CS) 2000 and Adaptive software Engine platform, rushing deployment throughout an enterprise however distinctive summon servers are installed.

fashions within the LG-Nortel IP mobilephone 8800 sequence latitude from entry-stage contraptions designed for ease of consume 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 title 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 consume IP technology to enable a wide compass 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 entire over the world who possess chosen to installation VoIP possess 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 possess 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 puss 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 dote 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 traffic located in Ghent, Belgium.

Nortel continues to execute on its three commitments to the cable provider provider market - new product edifice 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 valuable suggestions. Serving both service provider and traffic 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 back 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, quest recommendation from

certain tips covered during this press unencumber is ahead-looking and is realm to essential dangers and uncertainties. The results or activities anticipated in these statements may additionally compass 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 determination of the accounting considerations announced on November eleven, 2004, including the influence of discussions with the us Securities and change fee (SEC); the possess an sequel on of the administration adjustments announced on April 28, 2004 and August 19, 2004; the possess an impact on of the inability to meet Nortel's filing duties on usher amenities and public debt obligations; any abilities delisting or suspension of the business's or NNL's securities; the adversarial determination of litigation, investigations, highbrow property disputes and similar concerns; the sufficiency of Nortel's restructuring actions, including the work method introduced on August 19, 2004 as up to date on September 30, 2004, including the talents for bigger exact costs to live 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 actuality 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 back Nortel's typical route company activities; the dependence on Nortel's subsidiaries for funding; the feel of Nortel's described edge plans and deferred tax belongings on results of operations and Nortel's cash flows; Nortel's dependence on new product evolution and its means to foretell market claim 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 possess an sequel on of expense and product competitors; barriers to alien boom and world economic conditions, above entire in emerging markets and together with hobby rate and forex trade expense fluctuations; the possess an sequel 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 compress date or early settlement of Nortel's buy contracts; the feel of Nortel's give and outsourcing contracts that hold start and installation provisions, which, if now not met, might outcomes in the impregnate of colossal penalties or liquidated damages; and the long Run success of Nortel's strategic alliances.

For more information with admire to positive of these and other components, behold the most recent benevolent 10-Q/A and figure 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 aim 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 entire 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 sequence (See Supporting Methods for the queries they used). They then download metadata for each sequence 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 sequel not hold 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 consume 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. behold Supplementary Fig. 3 for a histogram of read counts per series.

Labeling using Cell Ontology terms

We consume 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 option of edge direction means that entire edges point toward the root nodes in the ontology.

We consume the title 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 consume some figure of dimensionality reduction to visualize and dissect 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 aptitude 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 figure 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 live taken as a reduced dimensionality representation of the data. These erudite 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. entire 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 live more efficient 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 travel nodes. Multiple genes are associated (and connected to) the identical node. They consume this grouping of the input genes as the first hidden layer of a neural network. Nodes in the next hidden layer will live constructed from travel 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. behold 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 possess 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 identical 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 identical class, while at the identical time penalizing minute 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 identical motivations as siamese networks, triplet networks moreover quest to learn an optimal embedding but sequel 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 identical 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 din that may possess occurred in their automatic term matching process. This is done by only keeping terms that possess at least 75 cells mapping to them, and then only keeping cells with a separate 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 possess a few hidden layers, so the edge of ReLU is less clear. They moreover experimented with different learning rates, momentums, and input normalizations (see web server for complete results).

Since their goal is to optimize a discriminative embedding, they test the attribute of their neural embeddings with retrieval testing, which is similar to the assignment 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 moreover 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 character when training and testing so that the test set is completely independent of the training set. They find entire cell types which near from more than one study, and hold out a complete study for each such cell character to live a fragment of the test set (sometimes referred to as the “query” set in the context of information retrieval). Cell types that sequel not exist in more than one study are entire 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 live 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 usurp since “neuron”, “hippocampus”, and “brain” are entire related cell types, and a model that groups these cell types together should not live penalized as much as a model that groups completely unrelated cell types together. They possess thus extended the NN learning and evaluation methods to incorporate cell character 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 consume 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 consume data from a separate 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 sequel 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. entire 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 possess used Single-Cell Differential Expression (SCDE) here29. For this, they used read counts rather than RPKM, as SCDE requires weigh data as input. This method builds an mistake model for each cell in the data, where the model is a compund between a negative binomial and a Poisson (for dropout events) distribution, and then uses these mistake models to identify differentially expressed genes. They moreover 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 identical 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 consume of meta-analysis of multiple DE experiments. The algorithm attempts to develop the best consume 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 possess 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 entire studies stored in the database. For this, they first consume the NN to reduce the dimensions of each of the input profiles and then consume approximate nearest neighbor approaches to match these to the data they possess pre-processed as they argue 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 live too slow. To enable efficient searches, they benchmarked three approximate nearest neighbor libraries: NMSLib31, rile (, 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 edifice and query runtime were tuned to trade-off a towering 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 rile 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 consume 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 entire query cells against entire cell types which is presented as a hierarchical clustering heatmap (Fig. 5a). entire visualizations are based on this matrix.

For each query cell qi and nearest neighbor nk, they cipher 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 character 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 entire 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 moreover perform further dimensionality reduction of the query via PCA to obtain a 2D nearest-neighbor style visualization against entire 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 moreover provide users with the list of experiments in their database that hold 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 back for stale versions of Xcode.

Row-Level Security in Azure SQL Data Warehouse

DBAs might possess 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 possess access to which data without the client application being any the wiser. Azure's own Azure SQL Database had moreover 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 lively Directory roles set up in a logical mode and not just stuffed every user into an admin group to deal with a mystery rights bug.

Microsoft reckons the functionality will live useful in scenarios such as a healthcare provider allowing nurses to only behold 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 sequel not meet the predicate. block predicates "will live 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 baleful personal helper became available to entire Windows 10 users this week, further ramping up the pressure on Microsoft's homegrown helper, Cortana.

Cortana was already reeling a itsy-bitsy from the loss of the VP answerable for the unloved technology and now faces competition from a standalone Alexa app.

Admittedly, the flush of Windows 10 integration is far deeper for Cortana. However, for PCs with "compatible hands-free" access, Alexa can live 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 possess compatible hardware, a click of a button in the Alexa app is sufficient to start the Amazon helper 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, order 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 travel global.

The app allows users to note 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 fragment of Office 365 or standalone (or via the Google and Apple app stores) but you'll necessity the Pro version to sequel 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 back 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 back for iOS 12) Redmond reckons it is time for the less-than-1-per-cent of the users of its pipeline noiseless clinging to the stale days to develop the move.

And if programmers don't pick the hint, benevolent stale 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 minute 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 possess 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. entire is well.

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

I'd recommend this prance to lots of folks  --  not everyone, by any means, but to anyone who isn't afraid to examine 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 supervision about freedom.

More at Medium

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

Steve Rummel: "Linux is my desktop of option when executing work for my clients  --  I possess more powerful tools that are easier to obtain and maintain and are of equal or greater attribute with lower licensing restrictions than their non-free counterparts. R, MySql, Python, Ruby, Apache, and the common aptitude of Linux to back older hardware makes my work life extremely pleasant. The available GUI layers (Gnome or Cinnamon) possess near a long route over the last decade, while the tremendous players possess done itsy-bitsy to develop things better in terms of the halt user interface."

David Blythin: "A lot of people win hung up on perceived limitations of endemic applications. For case GIMP isn't an exact replacement for Photoshop, nor is it intended to be, but I've yet to find anything I can't sequel in GIMP that I actually want to do. positive - PS proponents can near up with a list of stuff that GIMP can't do, but not a bit 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 behold the dispute that Apple's walled garden environment does not suit some peoples needs. However a balanced comparison should live inclusive of OS X, which is not restricted to the App Store and is traditionally a competitor of desktop operating systems dote Windows and Linux. The iPad Pro and iOS angle seems out of situation in this view piece.

Also, while data privacy concerns in Windows is worth mentioning, it's moreover overplayed. The reality is people should live more concerned about online data breaches, malware and management of online accounts. You don't possess to Look arduous 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 live diligent about. Linux is not the reply 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. possess you ever revised the specifications of future Microsoft Hardware? For instance TPM/UEFI/Secureboot? Why does Purism noiseless sell Hardware with TPM?

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

Sorry, mate. I speculate your so called license is an illusion."

Scott Wilson: "Between Microsoft trying to compel 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 option left is Linux. Chrome OS (linux) if you possess simple needs, or a trustworthy distro if necessity to sequel more complicated things. I love Fedora, but Ubuntu, Arch, CentOS, RHEL, SuSe, they are entire pretty much now."

Abiatha Swelter: "I've been using Mint on a laptop for a pair of years now and possess been pretty satisfied with it. The main sticking point with getting off Mac entirely is Adobe CS; there's no trustworthy Linux equivalent. My laptop is a Mythlogic/Clevo unit that I was able to win supplied with no OS, which was a nice benefit. It's a solid unit, if a bit hefty 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 slit my teeth on before discovering Debian in 1999, which I noiseless consume now. Linux has near a long route and it's awesome to behold people switching to open source alternatives. BSD too! Also, if your not close with it, check out the Wine project. You'd live surprised how many Windows apps Run on Linux now. :D"

Walton Pantland: "Good piece, but stirring to Linux is route simpler than he makes it appear. I am not at entire technical, but I switched 10 years ago, and it transformed my computing experience. Learning to character “sudo apt-get update" is not at entire 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 dote me."

Luis Anaya: "I possess 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 live my desktop system 100% of the time until this year in which I now consume a Xeon workstation running Debian Jessie. The understanding can live summarized as follows: Windows 10 was the stroke that broke the camel's back because I just sequel not supervision 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 endemic substitute I manage to consume them through the means of a Virtual Box running XP or through the consume of Wine being that programs dote Play on Linux makes it very convenient to consume Wine.

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

Rhy O'drinnan: "Great article! I possess two thoughts: I consume 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 entire the route around. I especially dote the Mate version and Mate menu, and personally favor the debian edition, as it's a rolling release, and I'm a indolent 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 viable exception of Android. Speaking of Android, I buy devices that can live formatted with Cyanogenmod and win rid of entire google stuff, and only consume the excellent open source market F Droid. It has most of what I necessity (mail, browser, music players, solitaire, etc..). trustworthy luck to you sir!"

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