Quick Link Explorer Gold v4.0.65 serial key or number

Quick Link Explorer Gold v4.0.65 serial key or number

Quick Link Explorer Gold v4.0.65 serial key or number

Quick Link Explorer Gold v4.0.65 serial key or number

Cisco Firepower Management Center 1600, 2600, and 4600 Getting Started Guide

To deploy the FMC you need information about the environment within which it will operate. The following figure shows an example network configuration for a Firepower deployment.

By default the FMC connects to your local management network through its management interface (eth0). Through this connection the FMC communicates with a management computer; managed devices; services such as DHCP, DNS, NTP; and the internet.

The FMC requires internet access to support Smart Licensing, AMP (Advanced Malware Protection) and TID (Threat Intelligence Director) services. Depending on services provided by your local management network, the FMC may also require internet access to reach an NTP or DNS server. You can configure your network to provide internet access to the FMC directly or through a firewall device.

You can upload updates for system software, as well as the Vulnerability Database (VDB), Geolocation Database (GEoDB), and intrusion rules directly to the FMC from an internet connection or from a local computer that has previously downloaded these updates from the internet.

To establish the connection between the FMC and one of its managed devices, you need the IP address of at least one of the devices: the FMC or the managed device. We recommend using both IP addresses if available. However, you may only know one IP address. For example, managed devices may be using private addresses behind NAT, so you only know the FMC address. In this case you can specify the FMC address on the managed device plus a one-time, unique password of your choice called a NAT ID. On the FMC, you specify the same NAT ID to identify the managed device.

The initial setup and configuration process described in this document assumes the FMC will have internet access. If you are deploying an FMC in an air-gapped environment, see the Firepower Management Center Configuration Guide for your version for alternative methods you can use to support certain features such as configuring a proxy for HTTP communications, or using a Smart Software Satellite Server for Smart Licensing. In a deployment where the FMC has internet access, you can upload updates for system software, as well as the Vulnerability Database (VDB), Geolocation Database (GEoDB), and intrusion rules directly to the FMC from an internet connection. But if the FMC does not have internet access, the FMC can upload these updates from a local computer that has previously downloaded them from the internet. Additionally, in an air-gapped deployment you might use the FMC to serve time to devices in your deployment.

Initial Network Configuration for FMCs Using Firepower Versions 6.5+:

  • Management Interface

    By default the FMC seeks out a local DHCP server for the IP address, network mask, and default gateway to use for the management interface (eth0). If the FMC cannot reach a DHCP server, it uses the default IPv4 address 192.168.45.45, netmask 255.255.255.0, and gateway 192.168.45.1. During initial setup you can accept these defaults or specify different values.

    If you choose to use IPv6 addressing for the management interface, you must configure this through the web interface after completing the initial setup.

  • DNS Server(s)

    Specify the IP addresses for up to two DNS servers. If you are using an evaluation license you may choose not to use DNS. (During initial configuration you can also provide a hostname and domain to faciliate communications between the FMC and other hosts through DNS; you can configure additional domains after completing intial setup.)

  • NTP Server(s)

    Synchronizing the system time on your FMC and its managed devices is essential to successful operation of your Firepower System; setting FMC time synchronization is required during initial configuration. You can accept the default (0.sourcefire.pool.ntp.org and 1.sourcefire.pool.ntp.org as the primary and secondary NTP servers, respectively), or supply FQDNs or IP addresses for one or two trusted NTP servers reachable from your network. (If you are not using DNS you may not use FQDNs to specify NTP servers.)

Initial Network Configuration for FMCs Using Firepower Versions 6.3 - 6.4 :

  • Management Interface

    The FMC management interface (eth0) uses the default IPv4 address 192.168.45.45, netmask 255.255.255.0, and gateway 192.168.45.1. During initial setup you can accept these defaults or specify different values.

    If you choose to use IPv6 addressing for the management interface, you have the option of using router autoconfiguration, or you must provide the IPv6 address, prefix length, and gateway. If your network uses DNS, during initial configuration you can provide a hostname to identify the FMC.

  • DNS Server(s)

    If your network uses DNS you can specify the IP addresses for up to three DNS servers during initial configuration. If you are using an evaluation license you may choose not to use DNS. (During initial configuration you can also provide a hostname and domain to faciliate communications between the FMC and other hosts through DNS; you can configure additional domains after completing intial setup.)

  • NTP Server(s)

    Synchronizing the system time on your FMC and its managed devices is essential to successful operation of your Firepower System. Configuring time synchronization is not required on initial setup, but we recommend that you configure your FMC to use trusted NTP servers. During initial setup you will need the host names or IP addresses of those NTP servers.

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Massive online data annotation, crowdsourcing to generate high quality sleep spindle annotations from EEG data

Abstract

Spindle event detection is a key component in analyzing human sleep. However, detection of these oscillatory patterns by experts is time consuming and costly. Automated detection algorithms are cost efficient and reproducible but require robust datasets to be trained and validated. Using the MODA (Massive Online Data Annotation) platform, we used crowdsourcing to produce a large open-source dataset of high quality, human-scored sleep spindles (5342 spindles, from 180 subjects). We evaluated the performance of three subtype scorers: “experts, researchers and non-experts”, as well as 7 previously published spindle detection algorithms. Our findings show that only two algorithms had performance scores similar to human experts. Furthermore, the human scorers agreed on the average spindle characteristics (density, duration and amplitude), but there were significant age and sex differences (also observed in the set of detected spindles). This study demonstrates how the MODA platform can be used to generate a highly valid open source standardized dataset for researchers to train, validate and compare automated detectors of biological signals such as the EEG.

Introduction

Sleep spindles are brief 10–16 Hz bursts of brain activity during stage N2 and N3 sleep. They are typically recorded from cortical surfaces by electroencephalography (EEG) and are markers of sleep dependent cognition1, early indicators of mental disorders2 or brain deterioration due to age3. Spindles follow a characteristic waxing and waning profile, and generally last 0.5 to 1.0 seconds in duration. These characteristics are predominately trait-like, and remain remarkably stable night after night within an individual, but vary between individuals4. A small but consistently observed decrease of the spindle density, amplitude and duration occurs with age5,6,7,8,9. Sex differences of spindle activity linked to memory or aging have been reported10,11,12,13, where women tend to be less affected by aging6,10 resulting a greater spindle activity (peak-to-peak amplitude14 and density4,7) in women than men, particularly in the elderly. Characteristics of spindles may index the underlying neuroanatomy involved in normal brain function, particularly in the processing of learning and memory, and have been related to intelligence15,16,17,18,19,20,21.

As well as their relation to biological processes, the detection of spindles is a key component in analyzing human sleep, as spindles are used to indicate the transition from stage N1 to N2 sleep during sleep scoring. However, detection and quantification of these oscillatory patterns by highly trained experts is time consuming and costly. Further, the definition of sleep spindles(A train of distinct waves with frequency 11–16 Hz with a duration > = 0.5 seconds, usually maximal in amplitude using central derivations)22 is not entirely precise, and experts disagree on variations of sleep spindles. As well, the EEG signal may be obscured by other signal phenomena, thereby limited human detection. Critical for the advancement of sleep science is the development of automated feature detection tools. Recent years have highlighted the power of machine learning methods in the biosciences to augment expert clinical judgment. For example, cardiologist level arrhythmia detection23, or seizure diagnosis24. Automated methods do not fatigue, are cost efficient, remain consistent, and are readily deployable. However, previous studies have suggested that there are important differences between human and algorithm detected spindles14, leading to conflicting results depending on how spindles were detected25,26. For instance, a significant decrease in sleep spindle density using visual scoring was observed in autism patients27,28,29, whereas an increase or no difference was found using an automated detector30,31. Similarly, in narcolepsy, a decrease of spindle density was observed with visual scoring32 but not replicated with an automated algorithm33. While automated methods show great promise for sleep science, they require large, highly valid datasets, which were not previously available. Here we introduce a large, open, highly valid dataset of human sleep spindles collected through crowdsourcing.

Crowdsourcing, which has been previously used to collect spindle data14,34,35, involves collating the judgments of a large number of human scorers to reach a high quality “gold standard” consensus. This data collection method leverages the “wisdom of the crowd” effect36, where the collective opinion of a group of individuals tends to be more accurate than a single expert. Crowdsourcing yields better spindle detection, and captures more generalizable spindle properties than single expert scoring because each scorer contributes only little, thereby reducing errors from fatigue and distraction, and we capture a diverse, unbiased opinion on what represents a true spindle, which is especially important given the imperfect agreement between experts. The idea of crowdsourcing for sleep science was first introduced by Warby et al.14, where segments of stage 2 sleep (from 110 subjects) were viewed by a mean of 5 experts and 11 non-expert Mechanical Turk (mturk) Workers. Agreement between experts (average individual f1 = 0.67 against gold standard) and the performance of the group consensus of non-experts against the gold standard (f1 = 0.67) were high, and non-experts outperformed the automated detectors. Unfortunately, due to privacy concerns, the polysomnography dataset used in this study is not openly available to the public, greatly restricting its use as a benchmark for algorithm validation. Ray et al.9 independently developed a similar paradigm to Warby et al.14 but used the openly available Montreal Archive of Sleep Studies (MASS)37. Each segment of EEG (from 15 subjects) was viewed by two experts and a mean of 18 non-expert mturk workers. Agreement between the non-expert consensus and the expert who scored in similar conditions than the non-experts was substantial (f1 = 0.81), but a moderate agreement was observed between the only two experts who scored MASS (f1 = 0.54), limiting the validity of the expert dataset of spindles. Similarly Zhao et al.35 collected spindles scoring in a crowdsourcing scheme from 5 experts and 168 non-experts (at least 20 non-experts per segment) and reported a high agreement between the non-expert and expert consensus (f1 = 0.78), unfortunately the dataset used is not open source. We aimed to build upon the success of these three studies and produce a large, open dataset of high quality spindles from both young and old subjects. Using this dataset, we ask: a) Can many non-experts match the quality of an expert technician with much lower cost and completion time? b) Do experts agree on spindle features, and if so, what are they? c) How do spindle features change across age and sex? Further, the conclusions that drive sleep science are often built upon spindles scored by non-technician researchers. Therefore, we added a non-PSG-tech “researcher” group, composed of graduate students, postdocs and faculty in the sleep science field and compare these to formally trained PSG experts.

To facilitate scoring, we developed a web-based open source online scoring platform, named MODA for Massive Online Data Annotation. The MODA platform allowed scorers from around the world to perform the spindle-identification tasks wherever and whenever they chose. While, in this study, we have used MODA for spindle scoring, it is an adaptable platform that could be easily used for the crowdsourced scoring of any EEG or biosignal-based annotation task. In this paper, we described how data was crowdsourced and analyzed. A number of Group Consensuses (GCs) were created by aggregating the scoring of many scorers, thereby removing idiosyncratic noise and increasing validity of the spindle dataset. GCs in this study were compiled from the three different user subtypes independently: PSG technologists (experts; exp), researchers (re) and non-experts (ne). The PSG technologists, who are trained and perform spindle scoring regularly as part of their work, are considered the experts, and their GC is designated the formal and highest-quality “gold standard” (GS) set of spindles of MODA. This GS spindle annotation dataset introduced here is freely available on the Open Science Framework38 and can serve as development and testing database for automated spindle detectors including machine learning methods to analyze EEG signals. We also evaluated the performance of seven previously published spindle detectors6,34,39,40,41,42,43 against our MODA GS, breaking down performance by age and sex, and thereby providing independent benchmarking (since none of these detectors have been optimized on the MODA GS) for sleep science’s most common used spindle detectors.

Results

Spindle dataset collection

Polysomnographic data from 180 subjects was sourced from the Montreal Archive of Sleep Studies (MASS)37. The dataset was split into two “phases”, where phase 1 consisted of 100 younger subjects (mean age of 24.1 years old) and phase 2 consisted of 80 older subjects (mean age of 62.0 years old). A subset of N2 stage sleep from the C3 channel was sampled from each subject (see methods for details). 25 sec epochs of this single channel EEG were presented to expert PSG technologists, researchers, and non-expert scorers via a custom web based scoring platform. Users identified the start and stop of candidate spindles, and indicated their confidence (high, med, low) for each spindle marked. In total, 47 PSG technologists, 18 researchers and 695 non-experts viewed 10,453, 6,636 and 37,467 epochs respectively in Phase 1. Phase 2 was viewed by 31 PSG technologists (7,941 epochs viewed). No scorers viewed the whole dataset, and the histogram of the number of scorer views per epoch image is shown in Fig. 1. A minimum number of scorers per epoch was crucial to compile a reliable gold standard (GS): the median number of scorers per epoch is 5 for the PSG technologists (Fig. 1a,b), 4 for researchers (Fig. 1c) and 18 for non-experts (Fig. 1d). More than 95% of all the epochs have been seen by at least 3 PSG technologists. Table 1 presents the number of scorers and amount of data scored for each user subtype and phase. Almost 100,000 candidate spindles were identified by all scorers combined.

Human group consensus

The collected scores include many candidate spindles, and some of them showed low agreement across scorers (an event scored as a spindle by some can be scored as “not a spindle” by others). To create our GS (dataset of the highest quality spindles from the Group Consensus (GC) of experts) we averaged scoring across experts, and kept (by thresholding) only the candidate spindles that exceed a desired minimum consensus between experts – termed Group Consensus Threshold (see Methods). The minimum consensus defined by the Group Consensus Threshold (GCt) was chosen to maximize the mean individual expert performance (see Supplementary Fig. 1 and Table 1) against the leave-one-out GS (the GS in which the evaluated expert did not contribute to the spindle scoring). We identified an optimum required consensus GCt between experts of 0.2 in phase 1 and 0.35 in phase 2. These GCts are similar to what has been previously reported14. The scorers’ performance was evaluated using a “by-event” f1 score (f1), which is the harmonic mean between the precision and the recall. Recall is the percentage of gold standard spindles correctly detected by a scorer (true positives divided by true positives plus false negatives i.e. completeness), whereas precision is the percentage of a scorer’s spindles that are part of the gold standard set (true positives divided by false positives + true positives i.e. exactness). This by-event performance depends on how similar the estimated spindle (marked by a scorer or detected by an algorithm) has to be to the GS spindle to be considered as a match (True Positive); the lowest similitude occurs when spindles are adjacent (no overlap between spindles) and the strictest similitude occurs when spindles are temporally aligned with the exact same length (100% overlap). Figure 2 presents the by-event performance of experts (as well as researchers, non-experts and algorithms) as a function of the overlap threshold between estimated and GS spindles. An overlap threshold of 0.2 (also previously reported14) was the highest threshold that maximized performance and was used for further analyses in the current study.

With the GC threshold and overlap threshold chosen, the gold standard consists of 5342 spindles (3338 in phase 1, 2004 in phase 2). The properties of these spindles are reported in Table 2. This set of GS annotations is freely available on the Open Science Framework38, and the corresponding EEG data can be downloaded from the Montreal Archive of Sleep Studies website (http://www.ceams-carsm.ca/mass/). See the Readme document on the Open Science Framework38 for details on how to obtain a license to download these data.

Performance of the human group consensus and automated detectors

A rigorous evaluation of spindle results from clinical and academic sleep studies hinges on quantifying the accuracy and biases of the spindle detection method used. Therefore, to inform future work, we evaluate the spindle detection performance of experts, researchers and non-experts. Human detection of spindles is still considered the highest standard; however, many recent publications have utilized automated methods to save time and cost. Therefore, along with evaluating the performance of humans, seven popular and previously published spindle detection algorithms6,34,39,40,41,42,43 were run on the EEG data (see Methods for details on the algorithms). We compared the by-event performance of each automated detector or human group consensus (GCre and GCne) against the GS, and the individual experts were evaluated against the leave-one-out GS to avoid reporting bias.

The mean individual expert f1 was higher in phase 1 (0.76) than phase 2 (0.65), suggesting that spindles are easier to score in the younger cohort. A mean individual expert f1 of 0.67 has previously been reported14 for a cohort similar to our phase 2. The f1 of the GCre and GCne was ~0.8, suggesting that the group consensus performs better than individual experts, on average (Figs. 2a, 3d). It is noteworthy that individuals (including individual experts, non-experts and researchers) that have very high or low f1 scores tend to be scorers that did not score much data (indicated by lighter colored markers in Fig. 3). Scoring a small amount of data and thereby not encountering the full variety of epochs could have resulted in artificially high/low individual scores.

Similar to human scores, the f1 of the detectors were slightly reduced in the older cohort compared to the younger cohort, except for a943 which remained the same (Fig. 2a,b and Supplementary Table 2). Top performance (based on f1 score) on the younger cohort (phase 1) was the GCre followed closely by the GCne. The a742 detector had the highest f1 in the younger cohort, closely matching performance of the average human expert (Figs. 2a, 3d). The highest f1 in the older cohort was reached by a9. Interestingly, a9 was the method most sensitive to the overlap threshold, as its performance decreases more rapidly than other methods as the threshold becomes more stringent (see methods). Therefore, spindles detected by the a9 algorithm and matching GS spindles are less perfectly temporally aligned (i.e. the start/stop and duration of spindles is less accurate) compared to the other methods. Detector a9 performance was followed closely by a7. We also evaluated the detectors performance against the GCre (see Supplementary Fig. 2a) or the GCne (see Supplementary Fig. 2b). The performance of the automated methods remained essentially the same (for more details see Supplementary Table 3).

Automated detectors had their own specific tradeoff between precision (how many detected spindles were matching GS spindles) and recall (how many GS spindles were detected), the most balanced algorithms were a4 and a7 (Figs. 3a,d and Supplementary Table 2). The highest f1 on the whole cohort (phase 1 & 2, 180 subjects) was reached by a7 (0.72 against the GS) which is the same as the average individual expert f1. This performance is followed closely by a9 with a f1 = 0.71, a9 showed a higher recall (0.8) but a lower precision (0.65) (Fig. 3d). Figure 3(b,c) shows the Precision-Recall plot of the individual re or ne and their GC (GCre and GCne respectively). Note that the majority of the individual researchers showed a high precision to the detriment of the recall (i.e. are overly conservative when marking spindles), and the resulting GCre is perfectly balanced with a GCt = 0. The performance evaluation of the detectors against the three different human references (GS, GCre, GCne) provided similar results (for more information see Supplementary Table 3). The number of spindles, and detailed performance metrics (True positives, False positives, False Negatives) for the GS, GCre, GCne and each automated algorithm are reported in Supplementary Table 4. The performance (as quantified by the precision, recall and f1-score) of the seven tested detectors were essentially the same as reported previously14,34,42,43. Note that the performance of a9 was slightly more balanced in the original publication43 than in the current study.

Spindle characteristics by-subject as a function of age and sex

Spindle activity decreases with age, and sex differences have also been reported3,4,5,6,7,8,9,10,11,12,13. We evaluated the age group difference between 100 subjects 18–35 years old and 80 subjects 50–76 years old, and sex difference between the 88 females and 92 males. We tested the spindle density measured as spindle per minute (spm), average maximum peak-to-peak amplitude (µV), average duration (s) and average dominant oscillation frequency (Hz) by-subject on the spindle dataset included in the GS (see Methods). A 2 × 2 ANOVA showed main effect for age and sex but no interaction on both for spindle density (age p = 0.0001 and sex p = 0.001) and average amplitude (age p = 1.5e-6 and sex p = 3e-8). The difference on the average spindle duration was significant only for age (p = 0.01). No significant effect was found for the dominant oscillation frequency of the spindle. Further analyses of the age and sex differences were performed with the non-parametric Mann-Whitney test (Fig. 4) since the spindle characteristics distributions were not all normally distributed based on the Shapiro-Wilk test. The spindle density in the GS was higher (p = 0.0002), average duration was longer (p = 0.008) and average amplitude was higher (p = 2e-06) in younger compared to older subjects (Fig. 4). The spindle density (p = 0.0008) and the average spindle amplitude (p = 1e-06) in the GS were also higher in females compared to males (Fig. 4). Supplementary Tables 2 and 3 contain detailed analysis of each detector’s ability to capture the sex and age trends present in the GS.

The average spindle activity reported in the previous crowdsourcing project14 was similar to our phase 2 (older cohort) despite a relatively high standard deviation across subjects. Warby et al.14 reported 2.3 ± 2 spm with an average duration of 0.75 ± 0.27 s, a maximum peak-to-peak amplitude of 27 ± 11 μV and an oscillation frequency mean of 13.3 ± 1 Hz. We measured a by-subject dominant oscillation frequency of 13.1 ± 0.8 Hz (see Supplementary Table 5).

Comparison of detection methods

When considering which method to use to detect spindles, automated or otherwise, it is important to understand which spindle properties are best captured by each. To this end, we computed the correlation of the spindle density and spindle characteristics between the GS spindles and automatically detected spindles for each algorithm (a2-a9) as well as GCre and GCne. The correlations for the spindle density in phase 1 (younger cohort, 100 subjects) are reported in Table 3. For phase 1, the correlation is higher for human GC than automated detectors. The GCne is slightly more correlated (r2 = 0.91) than the GCre (r2 = 0.88). The correlation for the detectors is low for the spindle density (r2 average across detectors is 0.37) and spindle duration (r2 = 0.32), but very high for spindle amplitude (r2 = 0.90) and high for spindle frequency (r2 = 0.69). The detectors a7 and a9 performed better than the average of the detectors, especially for the spindle density which their r2 were 0.73 and 0.85 respectively. The correlation coefficients for the detectors in phase 2 are reported in the Supplementary Table 6. Briefly, the correlation was higher for the spindle density but lower for all the other characteristics compared to the phase 1. Again, the detectors a7 and a9 outperformed the other detectors for the correlation with the GS spindle density with a r2 = 0.83 and 0.88 respectively.

We compared the spindle characteristics by-subject distribution of each detector (a2-a9) and human group consensus (GCre and GCne) to the GS for the whole cohort except for GCre and GCne using a Mann-Whitney test. The variance in spindle characteristics was much larger across detectors than across the three human subtypes (PSG technologists, researchers and non-experts) (Fig. 5 and Supplementary Table 7). The spindle density of a2 was much lower (0.9 spm, p = 9e-38) than the GS (3.8 spm), a3 (7 spm, p = 3.6e-25) and a8 (6.9 spm, p = 2.3e-34) were much higher than the GS. The average duration was much higher for a2 (1.15 s, p = 1.6e-33) and a9 (1.15 s, p = 2e-49) compared to the GS (0.78 s), but a3 (0.56 s, p = 4.7e-43), a4 (0.67 s, p = 1.1e-15) and a5 (0.5 s, p = 1.2e-48) were much lower. The average amplitude and oscillation frequency were about the same for all the detectors except a2 which showed spindles with greater amplitude (43 µV, p = 9.5e-30) than the GS (30 µV). The histogram at the cohort level (by-subject analysis) of the dominant oscillation frequency of spindles of the GS spindles or any of the automated detectors is unimodal, and does not support the hypothesis of decomposing the spindles into fast and slow spindles (Fig. 5d). Note that the slightly higher spindle density, duration and amplitude for the re and ne spindle dataset (Fig. 5) are biased due to the fact that only the younger cohort (phase 1) was scored by these groups (see Table 2 for the true comparison for the phase 1, “Phase 1 - Younger” column).

How many scorers are needed for crowdsourcing sleep spindle annotations?

Obtaining quality spindle scoring is costly and time consuming; knowing the number of scorers per epoch to achieve reliable results is worthwhile and may help to create future GS datasets. We identified that aggregating the scoring from two to four experts or researchers per epoch is optimum (Fig. 6a). However, three to ten non-experts were needed for similar performance (Fig. 6b). Zhao et al.35

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Monday, July 26, 2010

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Quarterdeck WebTalk 1.0 for Windows : s/n: 000-08D-00009 or s/n: 505-18D-50509 or s/n: 505-28D-50509

Quarterdeck WEBtALK 2nd Seat : s/n: 000-08H-00009 or s/n: 505-18H-50509 or s/n: 505-28H-50509

Quarterdeck CleanSweep 95 : 004-16c-68867

Quarterdeck Internet Suite 1.0 : 003-17j-55484

Quatro PRO v3.1 : DA246D10323488

Quatro PRO v5.0 for Windows : 1F945C10098127 or IA935A10031893

Quattro Pro v2.0 : s/n: DA245C10043771

Quattro Pro v3.1 : s/n: DA246D10323488

Quattro Pro v4.0 for Dos : s/n: PA247B10254847

Quattro Pro v5.0 -Dutch- or NL : s/n: IA945C10033381

Quattro Pro v5.0 for Windows : s/n: 1F945C10098127 or s/n: IA935A10031893

Queue v4.01 : Password: saladmin

Query for OS/2 : s/n: 5622-118

QUERYer V3.2.128 : Name: SiraX Company: DNG s/n: Q332-3408-0225-6960

QUERYer v4.0 : Name: Joseph Gonzales Company: Versus Inc. s/n: Q332-9764-2753-3503

QUERYer v4.00.0102 : Name: Monika Halkort Company: SHOCK s/n: QRY4-4299-7942-6447

QueryN MetaSearch v2.1 : Name: Dront Code: 72NDA8K
Quick Books Pro for Win95/NT : s/n: 0261556419 or s/n: 1234567879

Quick Books v1.0 for Dos : s/n: 382.71.904

Quick Books v2.0 for Windows : s/n: 296-00-111

Quick Books v3.0 for Windows : s/n: 1000164598 or s/n: 1002571428

QuickBooks PRO v5.0 with QuickBooks PRO Timer: s/n: 772-888-99957 or s/n: 107.000.27636

QuickBooks Pro v6.0D MultiUser : CD-Key: 7112-111-114-3476 Choose telephone registration and enter s/n: 1251-7311-1127

Quickbooks Pro 99 : s/n: 1030-066-593-8206 After you are done with the install, start the program and open the sample company. Drop down the file menu and click on REGISTER then select "register by phone" and enter: 0280-1895-6006

Quick Boot v2.65 : Code: 6343730

Quick Cab Professional v6.5 : Name: LOMAX [DSI] s/n: 540154816136

Quick Calculator v1.01 : Name: Darkzz [Crystal] s/n: 87536121

Quick Card v2.5 Win9x/NT : s/n: 76263294

Quick Charts v3.2 : Password: EASY2SEE

Quick Clean v3.21 : Name: Arfa [PCY] s/n: 83B2F28691219A1014
QuickCM v1.2 : Code: JPK X99 PLJ

Quick Color 3.22 : Name: Steve Hsu 5029 s/n: 08093B34

Quick Color v3.28 : Name: miSTER fRANTiC [dNG] s/n: 33214F6B

QuickColor v3.28D : Name: D-tRAdER (DSi) s/n: 190C7C5C

Quick Delphi Project Compiler v1.0 : Name: DeionXxX s/n: 4G4U-VAFK-05RI-YZ1G

Quick Dial V1.1 : Name: SiraX Code: 98779164 or Name: n03l Code: 2460145

Quick Dialer v2.1 : Name: tHeRaiN/PGC Key: 3279651204

Quick Dialer v2.2c : Name: Ringer Key: 3274922470

QuikEdit - Web Site Development Studio v1.0.21 : Name: Lee David Morrison s/n: rNUf-GMcK-SYri-

QuikEdit Web Site Development Studio v1.0.37 : Name: Lee David Morrison s/n: rNUf-GMcK-SYri-

QuikEdit Web Site Development Studio v1.0.38 : Name: Pete Aronson s/n: 2Chc-jrhF-QUrV-

QuicKeys v1.01 : Install: KJAFJYBY2RY4R FirstRun: LFG24Z85TF6US5R

Quick Folder Finder : 8738

QuikGrid v4.0 : Name: PhrozenCrew99 Code: 313047

Quick Install Maker 32bit v2.02 : s/n: QIM32-7713-48032-38

Quick Install Maker 32bit v2.03 : s/n: QIM32-7713-48032-38

Quick Install Maker 32bit v2.04 : s/n: QIM32-7713-48032-38

Quick Install Maker v2.5 Win95NT : s/n: QIM32-7713-48032-38

Quick Install Maker 98 v3.0 : s/n: QIM32-7713-48032-38

Quick Install Maker v3.01 : s/n: QIM32-7713-48032-38

Quick Install v3.02 : s/n: QIM32-7713-48032-38

Quick Install Maker 16bit v4.02 : Name: GHOST RIDERS Code: QIM16-5529-28632-414

Quick Install Maker 16bit v4.03 : Name: GHOST RIDERS Code: QIM16-5529-28632-414

Quick Install Maker 16bit v4.04 : Name: GHOST RIDERS Code: QIM16-5529-28632-414

QuikLink Explorer Gold Edition v3.0 BETA Build 325 : Name: Alan Goldman s/n: a@93-6AFS-Wqd

QuikLink Explorer Standard v3.0 Build 325 : Name: Alan Goldman s/n: nWYi-zFIF-Zvd

QuickLink Explorer Gold Edition v3.0 Beta Build 338 : Name: Alan Goldman s/n: a@93-6AFS-Wqd

QuickLink Explorer Gold v3.0 PR1 build344 : Name: Arthur Fawcett Jr. s/n: w97B-QiLO-bph

QuickLink Explorer Standard v3.0 PR1 Build344 : Name: Arthur Fawcett Jr. s/n: BQKI-N7GN-Yod

Quick Link Explorer Gold v3.0370 : Name: Delphic Code: 9146677763139349120000

Quick Link Explorer Gold v4.0.32 : Name: Gustavo Hideyuki Ono Garcia Code: Eh@9-82ER-bld

QuikLink Explorer v4.0.39 Gold : Name: Gustavo Hideyuki Ono Garcia s/n: Eh@9-82ER-bld

Quick Link Explorer Gold v4.0 Beta1 : Name: Delphic Code: 9146677763139349120000

QuikLink Explorer v4.0.41 Gold : Name: Pete Aronson Code: 0xvt-MGGO-Yvd

QuikLink Explorer v4.0.65 Gold : Name: Gustavo Hideyuki Ono Garcia Code: Eh@9-82ER-bld

QuickLink MessageCenter v3.2 : s/n: 980122053895

QuickMail Pro v1.53 : Name: PREMiERE Company: (Anything) Key: GVZKG7FMV5N3J5

QuickMem v1.0.4 : Name: MisterE[iNSiDE] Code: 36234291901144.4

Quick Menu III +v3.1b for Dos : pc s/n: 1157821GNIPPGS network s/n: 1157821BNNUKJN

QuickNews v0.4b : Name: Team DEMiSE'98 Code: 137164725

Quick News v4.0 : Name: REKiEM / PCY '99 Code: 132315061

QuickNic v1.01 : Name: Linda Yudka Code: 1655-MOSIBOOILG

QuickObjects v1.1.21 : Name: TUC PC99 Company: Phrozen Crew s/n: `=xi|,5^db=~Ri+=c~d+do?%5Uc3UV

Quick Paint v1.23 : Name: knoweffex Key: 1234321 Code: -aw6-anGMU9v-aa

Quick Paint v1.39 : Name: knoweffex Key: 1234321 Code: -aw6-anGMU9v-aa

Quick Paint v1.39.9 : Name: JUANDAPC Key: 12121212 Code: -d8T-bt-dIr-aFg-dc

Quick Paint v1.50 : Name: Lukundoo [HPA] Key: 3000104 Code: -b7-aY-dZr-bS9e-a4-bM-a8TQ-cO-bR

QuickRef Project Assistant v2.2 : Name: Versus Code: BLZBEDSA

Quick Report Artist v3.0.4 for Delphi : Name: TheBrabo s/n: AX-GG-LD-QR97

Quick Restart for Dos and Windows : s/n: 5747462262790A

Quick Restart v1.5 for Windows : s/n: 00617051QR1

Quickrun v2.01 : Name: Sexyboy s/n: 3097-25-17503

Quick Settings 1.0.1 : Name: davy - blizzard Code: frEW#&r

QuickSite 2.5.3 : RegCode: 11111-11/11-1111111111

QuickStart v99.1a Final : Name: MoWAX [Nobliege] s/n: 1806512746

Quicktest 1.5 & Astra Site Manager v1.08: Name: Pirate Company: Pentium Inc. s/n: 999000023AS09128c354

Quick Time v2.0 for Windows : s/n: 040904E4

QuickTime 3.0 for Win 95 : Name: Mad Hacker s/n: 085E-C714-0BC8-B2E1-1301 or Name: John A. Haverty s/n: 3FDF-F69E-5A79-9088-BD63

QuickTime 3 PRO : s/n: BBA5-2362-4363-99DE-2487 or s/n: 3231-0FD8-1D51-2E93-3432 or s/n: C3C8-8E43-94C6-6730-9549 or s/n: 1C61-1CD5-4EDD-3216-3243 or s/n: 675E-DF2B-8E31-126F-4233 or s/n: 1660-49F0-C751-5BB0-2343 or s/n: D362-7F9D-0C6D-0E71-7576 or s/n: 1B44-D393-EA30-C405-9348 or s/n: 90C9-5F2A-ADB6-4DE4-3245 or s/n: 201E-C550-D94F-AC5C-9696

QuickTime Pro V3.0.2 : Go to control panel, double click on quick time icon and enter: Name: Mad Hacker Company: TRPS s/n: 085E-C714-0BC8-B2E1-1301

QuickTime Pro Ver 7.0.2.120
Apple MOV movie player and codec
(Name: Dawn M Fredette S/N: 4UJ2-5NLF-HFFA-9JW3-X2KV)

QuickTime v4.0 Pro : Name: Batman Company: Dark-Knight s/n: 72D5-4A42-9D31-E31A-5965

Quick To-Do PRO v3.0 : Name: BiGMoM / MANiFEST Licenses: 10 s/n: 4227223A10287A

Quick To-Do PRO v3.1 : Name: BiGMoM / MANiFEST Licenses: 10 s/n: 4227223A10287A
Quick To-Do Pro v3.21 : Name: BaRT SiMPSoN Licenses: 978 s/n: 3C3E015DEE4357

Quick View Plus! v3.0 or 3.0.3 for Win95 : s/n: QVP0036131002

Quickview plus v4.0 : s/n: GTBFEYVU

QuickView Plus v4.5 for Windows 95 : s/n: PH-706532

Quicken Home & Business 98 Beta 3 for Win 3.x: Key: 1043886215

Quicken Home & Business 98 Beta 3 for Win95: Key: 1043886215

QuickRun v1.3 : 3B45FCB8010000007401 (Replace 74 to 75)

Quick Settings V1.0 : Name: gcrack s/n: FYB!#*G

Quick To-Do '98 v2.10 : Name: Versus s/n: 2000653

Quick To-Do 98 v2.22 : Name: Black Thorne / Phrozen Crew s/n: 2002424

Quick To-Do 98 v2.42 : Name: MANIFEST s/n: 2000604

Quick To-Do 98 v2.43 : Name: MANIFEST s/n: 2000604

Quick To-Do 98 v2.44 : Name: MANIFEST s/n: 2000604

Quick Version Update v2.01 : Name: TUC Company: PC99 s/n: 8s059G9253N138

Quick View Plus v3.0 : s/n: QVP0036131007

QuickWeb HTML Editor v3.0 : Code: r30v04b73

QuickWeb HTML Editor v3.1 : Code: r30v04b73

Quick Web HTML v3.02 : Name: PGC Code: r30v04b73

QuickZip V1.0 Win95/NT : Name: SiraX Code: 17992810044265

QuikASP v1.0 : Name: Gayla Rayworth s/n: QSP-359311-28556 EXE Unlock: qasp0430

QuikLink Explorer v3.0 PR1 b334 Standard Edition: Name: Gustavo Hideyuki Ono Garcia s/n: BQKI-N7HT-bpf

Quill v1.0 : Name: Ringer Code: 34f0SRX

Quintessential CD v1.1 : Name: CORE CMT License: 5edf088f

Quintessential CD v1.27 : Name: Ringer License: a22e02f9

QuipSig v1.2 : s/n: 103-167349

Quite Imposing Plus v1.0 : License: 0522-2701-4448-8182 Code: 6636

Quixote : s/n: 000-16K-00009

QuizPlease v97.1a : Name: REBELS Code: D-445-489

Quota Manager for Windows NT 2.6.1 : RB1J52PC975O

Quotation Organizer v2.0 : Name: tYruS@c4n.edu s/n: 1555554

QuoteJava v1.32 : Name: NiTR8^ s/n: NPLIRJGD

Quote Ticker Bar v5.0 : s/n: RnJpZGF5 RegNum: aVDdz3194p

QuoteWatch - Catch the Wave v3.50 : s/n: 7004459

QuoVadis v1.5.2 : Name : Linda J. O´neil Address: 475 Water Street Ort: USA-23704 Portsmouth s/n: 0126-L-TE Code: 84649524225

QV (Picture viewer) : Name: Crack da WareZ s/n: 32000

Qv-Autocam v1.4 : Name: Black Thorne s/n: BEPGCKR

QVCS v3.1 : Date: 05/16/1998 ID: 191980516-DL1L

QVCS v3.2 : s/n: 191980516-DL1L

QVCS v3.2a : s/n: 191980516-DL1L

QVCS v3.2c : s/n: 191980516-DL1L

QVCS v3.2D : s/n: 191980516-DL1L

QVCS v3.2e : s/n: 191980516-DL1L

QVCS v3.3 : s/n: 191980516-DL1L

QwikSchedule v3.0 : Name: Gorgeous Ladies Of Warez Code: 5601946

QwikSchedule v4.0 : Name: Gorgeous Ladies Of Warez Code: 5601946

QwikSchedule v4.0.2 : Name: Gorgeous Ladies Of Warez Code: 5601946

QX-Tools v4.0 : Name: RyDeR_H00k! Company: UCF s/n: QCE-400-000-653-000000

Q*Wallet v1.1 : Name: blizzard s/n: O4Z5U8X1L9Z4

Q*Wallet v1.1.1 : Name: accz of blizzard s/n: l8y7s8o6i6h8

Qwallet v1.2 : Name: TheDon[Fluke] s/n: S8T4A7Q4G4P5

QWallet v1.3 : Name: kaN[LSD] s/n: DR-206-1234567

QWallet v1.3b : Name: Arfa [PCY] s/n: NS-106-DR-206-27

Q Recovery 98 v1.24 build 186 : Name: jog Company: DNG Code: 8956-4168

Q-Recovery98 Professional v1.26 Win9x/NT : Name: Fully Licensed User Company: Fully Licensed Company Code: 66666651 Make sure to use the provided name and company during the installation.

Q-Recovery v1.26 build 190 : Name: draXXter Company: [Faith2000] s/n: 29292929

QACP v1.0.7 : s/n: 254048027

QACP v1.0.8 : s/n: 254048027

QAPlus v7.0 for Windows 95 : s/n: 24C1911

QAPlus/FE v5.30 : s/n: 12345-6789-0

QBIK Wingate 2.1 pro Win95/NT : Name: iBC s/n: f0271b3de5a25f918bb17d2a

qClick v1.01 : s/n: EMBB8 G5ANW 71DPG TKCRN

Qdesign I-Media Audio Mpeg v2.0 : s/n: Radium1998

QEMM 386 v7.03 and v7.04 : 001-32H-72414

QEMM 386 v7.5 : 010-17F-95293 or 011-17F-00009 or 017-17F-08319

QEMM 386 v7.52 : 000-000-00009 or 213-07H-70123 or 213-07H-70123 or 133-42H-58856 or 123-70H-03217

QEMM 386 v7.53 : 001-32H-72414 or 010-17F-95293-0123

QEMM v7.00 : s/n: 103-27E-53601 or s/n: 202-67E-80502 or s/n: 201-17E-80331

QEMM v7.03/v7.04 : s/n: 001-32H-72414 or s/n: 111-62H-44044 or s/n: 300-22H-22444

QEMM v7.50 : s/n: 010-17F-95293 or s/n: 011-17F-00009 or s/n: 017-17F-08319

QEMM v7.52 : s/n: 000-000-00009 or s/n: 000-252-47729 or s/n: 001-17H-72414 or s/n: 101-02H-90990

QEMM v7.53 : s/n: 001-32H-72414 or s/n: 405-22H-00500 or s/n: 501-62H-72405

QEMM v8.00 : s/n: 000-18A-40708 or s/n: 444-999-23679 or s/n: 000-08A-04681

Qemm97 v9.0 : s/n: QEDU9000108543

QFax 98 : s/n: 7002-0229-6330-3954

Q Folder 95 : Name: The Key Code: 370A9B91
QFront v?.?? : Name: The GuaRDiaN aNGeL bbs: G.!.$ s/n: 33-3333

QHost V5.0 Beta1 : Owner: SiraX Company: [Revolt] s/n: SiraXRV-MKVM74R

qHTML v1.0 : Go to Help, Register and enter: Code: 414-985-7054

Qif 2 Iif Converter v2.01 : s/n: 8921-1234567-59924

QImage Pro v3.3.2 : Name: TEX99 s/n: 46400063

Qmodem : Name: Batman s/n: 62758

QModem +v4.6 for Dos : Name: The GuaRDiaN aNGeL s/n: 51672 or Name: TeLLeRBoP s/n: 29323

Qmodem Pro v1.10 for Windows : s/n: 92-0069

Qmodem PRO v1.53 : 66-3454

Qmodem Pro v1.53 for Dos : s/n: 66-3454 or s/n: 86-0001 or s/n: 90-1444 or s/n: 90-0892 or s/n: 90-0859

Qmodem Pro v2.0 for Win95 : s/n: 569065 or s/n: 569069 or s/n: 57-17240 or s/n: 55-8355 or s/n: 92-0069

QmodemPro for '95 : 123-456-789

Qoole 2.31 : Name: FreeWilly s/n: 8788HN17988YH11178NG16811WR1 or Name: CU Chephren s/n: 7586rp-6869dt-1688kr-8203gw

QPEG V1.4b : (edit QPEG.REG) line #1: oere - okwar line #2: 2C-D05940F512345678804704956DF1A2C4

QPEG V1.4b : (make textfile QPEG.REG) line #1: oere - okware line #2: 2C-D05940F512345678804704956DF1A2C4

QPV/386 1.6c : (edit QPEG.REG and put this file in your root/path but not in your Qpeg directory) line 1#: CERE- The GuaRDiaN aNGeL line 2#: 5F-717746491234567821690229DA8CD47B

QRDesign V1.04 For Delphi-QuickReport : Password: banta-7013-dowers

QRDesign V1.05 For Delphi-QuickReport : Password: banta-7013-dowers

QREAD v2.1 : Name: Registered s/n: sWZNgrSmEbONAMYv

QReport Artist v2.1a For Delphi : Name: aerosmith Code: AX-GG-HM-QR97

Qstres 5.0 : s/n: Q598051003

Quack Sound Effect Studio v3.0.2 : First: CORE/JES Sur: CORE s/n: 00000000 Pass: 135097AA

Quad Blocks v1.1 : s/n: QUAB4468

Quake 2 server frontend v1.2 : Name: Norway/Revolt98 s/n: 90103035

Quake Name Editor v1.1 : Name: BuLLeT - [PC 98] s/n: 1A08FFF8

Quake Name Editor v1.1 Build 2 : Name: Goofer [DeMoN] s/n: 22702FEC

Quake Name Maker v1.2 : Name: BuLLeT s/n: 310589

Quake Spy V5.1 : User Name: Lost Soul [uCF] E-mail address: UNITED CRACKING FORCE s/n: cT2b-6qhs-3R57-5GXR

QuakeSpy (Any ver) : Name: CDWarez Email: CDWarez@rules.the.world.com RegKey: aafX-R49s-rH2d-3fc9

Qualitas DisPatch v1.0 for Windows : 100 3520 10180 or 100 7610 30040

Quant v3.0 Beta : Name: CORE/DrRhui s/n: 7149-6953-75

Quantam Mechanics For Photoshop 4.0: s/n: mindseye
Источник: [https://torrent-igruha.org/3551-portal.html]
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What’s New in the Quick Link Explorer Gold v4.0.65 serial key or number?

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System Requirements for Quick Link Explorer Gold v4.0.65 serial key or number

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