Category Archives: Evolution

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Brandt, M. et al. Human population growth offsets climate-driven increase in woody vegetation in sub-Saharan Africa. Nat. Ecol. Evol. 1, 81 (2017).

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Predicting the evolution of the Lassa virus endemic area and population at risk over the next decades - Nature.com

Dynamic application security testing (DAST) tools have been widely used for more than a decade, but there still exist misconceptions of what they can and can't do. The good news is that modern DAST tools far outstrip the abilities of their legacy forbears, making them essential components of any modern software development life cycle (SDLC).

The fundamental ability of a DAST tool is to conduct an automated pen-test of a web application -- essentially, to test application security by attacking a web application as a hacker would, probing for flaws. That's still the case, although modern DAST tools now go much further.

Legacy DAST tools, which include many of the free and open-source versions, give you strictly black-box insight into the workings of a web app. They can only tell you what's going in and coming out.

If they discover any vulnerabilities, legacy DAST tools can't provide any proof that the vulnerabilities are actually exploitable. It's up to the developers using the legacy DAST tools to test the potential vulnerabilities, leading to a potentially huge amount of time chasing down false positives.

Furthermore, legacy DAST tools often can't be used until a piece of code approaches the production stage, as most DAST tools can test only stand-alone working binaries. The tests often need to be triggered manually.

With legacy DAST, "you could scan many assets to see what you're working with, but for detailed analysis, you had to rely on manual inspection," explained Invicti's Zbigniew Banach in a 2020 blog post.

Modern DAST tools go far beyond these rudimentary abilities. They can often provide proof-of-concept exploits for discovered vulnerabilities, saving developers a lot of time that might otherwise be spent chasing down false positives. (DAST software maker Invicti calls this "proof-based scanning.")

Modern tools also are less strict about where in the software development life cycle they can be deployed and are able to test bits of code that legacy DAST tools might not have been able to handle. This lets developers get an early start on finding and solving problems.

"You can scan for vulnerabilities as soon as you have runnable code, which means from the first commit for most modern frameworks and trigger incremental scans automatically as part of the pipeline," wrote Banach in a 2022 blog post.

These modern tools can also run in the background, constantly testing code during the seemingly endless cycle of update-test-deploy-repeat and letting developers focus on their core duties.

"DAST can run any time of day and night, as often as you need," wrote Banach. "This is vital for continuous integration pipelines, where you can't organize a penetration test for every single build."

Many modern DAST tools also have additional features that embed them deeper into an SDLC, enabling secure coding across the development process. For example, some DAST tools can now scan for and discover web assets, even those that developers may have forgotten about.

They can also be integrated with bug-tracking platforms like Jira or ServiceNow, continuous integration/continuous development (CI/CD) tools like Jenkins or GitLab, and interoffice messaging programs like Slack or Microsoft Teams. Some modern DAST tools even come with different compliance modules to make sure the software being tested conforms to PCI-DSS, HIPAA or ISO 27001.

Modern DAST tools have also learned to make up for the shortfalls of their legacy forbears. The first generation of DAST tools often had trouble with custom authentication and business logic, so their descendants have learned to adapt to those. Likewise, modern DAST tools can often connect to Amazon Web Services environments for off-premises testing.

Finally, some modern DAST tools, such as Invicti's, include an element of SAST (static application security testing) to get a look at the underlying code and thus provide a view of an app's security from both outside and inside. This is often called interactive application security testing (IAST), but like SAST, it's often tailored to specific programming languages and can't be run independently like DAST tools.

"Simply put, a modern DAST solution is the only way to get a complete picture of your web security posture and take action from day one," wrote Invicti's Zbigniew Banach in a 2020 blog post.

So what should you consider when you're shopping for a DAST tool? One of the most essential features is the ability to "prove" that discovered vulnerabilities are actually exploitable and worth fixing.

"Do not consider solutions that cannot provide confidence and evidence of identified vulnerabilities," states a Web Application Security Buyer's Guide provided by Invicti. "Every vulnerability that cannot be confirmed with 100% confidence by your software must be verified manually, breaking any development automation and consuming time and security team resources."

You should check to make sure that the DAST tool has a modern crawling engine (preferably based on Chromium), can scan the internet for websites and domains belonging to your organization, can import standard API definition formats, and can scan for "blind" vulnerabilities that might not yield immediate outputs but could cause trouble down the road.

"If your vendor or software maker mentions terms like misconfigurations, open databases, and vulnerable libraries, there is a good chance that they support the discovery of many different types of web application security issues, not only web vulnerabilities," state Invicti's buying guide.

You'll also want to make sure that the tool can get past any custom authentication or business logic that your software may throw in its path. You might have to hold the software's hand to get past these obstacles, but any DAST tool that can't work even in that scenario should not be considered.

Last, you'll want to see how well the tool integrates with software that already exists in your development environment.

"The more integration capabilities a [DAST] solution has, the more time you will save when setting it up and using it," says Invicti's buying guide.

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Evolution of DAST: Beyond the foundation | SC Media - SC Media

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As those of you who read my column know, I like to share the interesting history of food. Food is a blessing many of us never think about. It is available to us anytime, hungry or not. Unfortunately this is still not so among many people in various countries even today.

I recently read a book about the history of how we cook and eat. The title is Consider the Fork, by Bee Wilson. You might enjoy reading it. Here are a few interesting tidbits from the book. There is much information behind each discovery.

The history of food begins with fire. We do not know how the first fire was made, how people became interested in it, nor how much fuel could be found to keep it fed. How did they restart it again if it ever went out? Before fire was used with food, people ate whatever they found. It may have been raw edible nuts, meat, insects, plants, berries, fish or sea food, if that was available. Fire revolutionized their diets.

In the beginning people had to learn how to cook using fire. Other than a spit construction, hot rocks or shells, there was nothing to use in, on, or above the fire to hold water or contents. There were no pots or containers. But people are very creative and figured out how to make containers out of various materials that would survive in or near the hot fire. Hot rocks were added inside the container to cook the food. A stick could be used to stir the contents.

Primitive ovens were eventually built. Later on as ovens were being refined, a piece of paper could be put in the oven to check for the temperature. The heat changed the color of the paper.

Having an oven to bake in brought many changes with food. Centuries ago it was found that adding an egg to batter created a lighter product that rose in height and was more tender. Cakes became popular! The sugar, if one had some, came in a 5 to 50 pound block and was chipped off when needed. As clocks were not available some foods were cooked according to the time one said a prayer. The cook knew how long it would take to say a Pater Noster (Our Father) or other prayers they knew by heart.

When cookbooks were being written there were no standardized measuring items. The ingredients were referred to by the size or shape of a familiar object such as a cup, an egg, a walnut, a handful or pinch. The recipes turned out pretty much okay as the cook used the same object when they cooked. Compare that now with standardized measuring devices, the cup, quart, etc., which American Fannie Merritt Farmer devised and promoted. However, other countries use the metric system and a scale is often used to more accurately weigh ingredients.

When ice became an industrial commodity, the railroad changed the diets of people as food could be kept cold while being transported. Another commodity we may use at times we cook may be opening a metal can. The canning procedure was invented by Nicolas Appert during Napoleons war with Britain in 1795. However, there was no can opener until 50 year later! One had to be inventive to open the can.

Do you use an ice cream maker? One of the first ice cream makers was invented by Nancy Johnson and took 3 minutes to make. However, unknown to her, the cheap zinc used was a poisonous metal. Tupperware was sold to help keep food fresh in your new-type refrigerator.

For an experiment, take your wooden spoon and look at it. A very simple plain tool many cooks use. What interesting history could be associated with it? The first paragraph of the introduction to the above mentioned book is about the wooden spoon. Knife, fork and spoon inventions are also very interesting.

The next time you place a pan containing water and a food ingredient on your hot stove burner, think about the miracle of things we take for granted.

Experiments, mistakes and technologies, past and present, and also new ideas, now play a large part in our foods and appliances we use.

Something to Think About: Fire, probably the greatest (discovery) excepting language, ever made by man. Charles Darwin, on cooking.

(Place on a hot rock to cook. Or use the BBQ.)

1 Tablespoon milk or cream

1 teaspoon dried parsley flakes

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8 to 16 strips broiled bacon

Beat the eggs, cream, parsley, garlic and allspice. Coat the cleaned trout inside and out with the mixture. Put 1 to 2 strips bacon in each trout and place in an oiled wire broiler basket or on a greased hot grill. Broil fish over hot coals for 20 minutes or till fish flakes with a fork. Turn once. Serve with lemon wedges.

(To whip fluffy egg whites, use your bundle of tied reeds and hand whip 1/2 to 3 hours. Or use your electric mixer.)

1/4 teaspoon cream of tartar

For 9 pie, separate eggs. Whites will whip fluffier if at room temperature. Beat egg whites with vanilla and cream of tartar till soft peaks form. Gradually add sugar, beating till stiff and glossy and all sugar is dissolved. Spread meringue over filling sealing meringue to edges of pastry to prevent shrinking. Bake in a moderate oven (350 F.) 12 to 15 minutes or till peaks of meringue are golden brown.

(Place in your ice box to set. Or use your refrigerator.)

18 graham crackers, crumbled

2 heaping Tablespoons powdered sugar

1 (10-1/2 ounce) pkg. small marshmallows

1 can prepared cherry pie filling

Mix the graham cracker crumbs, butter and sugar. Put half of this crumb mixture in the bottom of a 7x11 pan. Mix the whipped cream, powdered sugar and marshmallows and place half of this mixture over the crumbs in the pan. Next spread on the pie filling, then the rest of the cream mixture. Top with remaining cracker crumbs. Refrigerate overnight.

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The evolution of cooking and subsequent inventions - AgUpdate

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"I think whats missed over time is the burden of the treatments we use. What are the side effects to the treatments for myasthenia gravis? You had these situations where patients are doing better from a Myasthenia standpoint but having other symptoms that arent acceptable to them."

Over the past century, the prevalence of myasthenia gravis (MG) has increased; however, mortality has declined because of the development of effective treatments. Some of the first treatments for the disease began in the 1930s, followed by more common use of corticosteroids and plasma exchange in the 1960s. Over the past 2 decades, there have been more notable approvals, such as mycophenolate mofetil in 2008, rituximab (Rituxan; Genentech) in 2012, eculizumab (Soliris; Alexion) in 2017, and most recently, ravulizumab (Ultomiris; AstraZeneca) in 2022.

Ravulizumab, FDA-approved in April, became the first long-acting C5 compliment inhibitor for patients with MG, representing another feat for the community. At the 2022 American Association of Neuromuscular and Electrodiagnostic Medicine (AANEM) annual meeting, September 21-24, in Nashville, Tennessee, Nicholas Silvestri, MD, presented a talk on the evolution of treatment options for MG, including the common immunosuppressives used. Additionally, he detailed the efficacy and safety profiles of these therapeutics and highlighted how each are used when inadequate responses are found.

Silvestri, a clinical professor of neurology at the University at Buffalo, sat down with NeurologyLive at AANEM 2022 to discuss his presentation, along with the significant changes in the treatment paradigm, improvements in overall efficacy to these agents, and the need for increased awareness of the negative side effects.

Click here for more coverage of AANEM 2022.

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Evaluating the Treatment Evolution of Myasthenia Gravis and Use of Immunosuppressants: Nicholas Silvestri, MD, FAAN - Neurology Live

In the late 90s, Mitsubishi wanted to take its Dakar program to a production-based class. The company's Pajero Evolution racer, however, was a far cry from the road car that gave it a name. The solution was a homologation special, a three-year run of road-going Pajero Evolutions. Just a few thousand of these unique super-SUVs were ever built, all sold in Japan. 25 years have passed, which means the earliest Pajero Evolutions are finaly legal for import into the U.S. and the long-forbidden homologation special is now available to American buyers. That means the timing of this Pajero Evolution's sale could not be better.

The car, currently in Scotland, is about to be listed on Collecting Cars. While the car does not have a full listing yet and the auction has not actually begun, the published photos show a particularly well-kept white Pajero Evo. This particular car is an automatic, but the site also has a manual 1997 model coming up for sale soon in Australia if that's a dealbreaker for you. Previous Pajero Evos on the site have sold as high as $52,000 and as low as $16,000.

While it seems to be in pristine condition, this car has not lived in a garage waiting to be sold as a collector item elsewhere. A listed 180,000 kilometers translates to roughly 112,000 miles on the odometer, a well-used life of being driven for a car from 1997 even if that car weren't a Japan-only Dakar homologation special.

Excerpt from:

Buy This Glorious Pajero Evolution, Live Out JDM Off-Road Dreams - Road & Track

Ethical statement

This research project was ethically reviewed and approved by the Ethics Committee of the Instituto Gulbenkian de Cincia (license reference: A009.2018), and by the Portuguese National Entity that regulates the use of laboratory animals (DGAV - Direo Geral de Alimentao e Veterinria (license reference: 008958). All experiments conducted on animals followed the Portuguese (Decreto-Lei n 113/2013) and European (Directive 2010/63/EU) legislations, concerning housing, husbandry and animal welfare.

The ancestral invader E. coli strain expresses a Yellow Fluorescent Protein (YFP), and carries streptomycin and ampicillin resistance markers for easiness of isolation from the mouse feces [galK::amp (pZ12)::PLlacO1-YFP, strR (rpsl150), lacIZYA::scar]. An E. coli strain used for the in vivo competition experiments is isogenic to the ancestral invader but expresses a Cyan Fluorescent Protein (CFP) and carries streptomycin and chloramphenicol resistance markers [galK::chlor (pZ12)::PLlacO1-CFP, strR (rpsl150), lacIZYA::scar]. The resident E. coli lineage was isolated from the feces along time using McConkey + 0.4% lactose medium, as previously described9. All the resident clones sampled from each mouse belong to E.coli phylogenetic group B9.The invader E. coli strains (YFP and CFP) derive from the K-12 MG1655 strain (DM08) and exhibit a gat negative phenotype, gatZ::IS112. The resident E. coli clone used for the competition experiments in the mouse gut expresses a mCherry fluorescent protein and a chloramphenicol resistance marker, allowing to distinguish the invader and resident strains in the mice feces.

E. coli clones were grown at 37C under aeration in liquid media Luria broth (LB) from SIGMA or McConkey and LB agar plates. Media were supplemented with antibiotics streptomycin (100g/mL), ampicillin (100g/mL) or chloramphenicol (30g/mL) when specified.

Serial plating of 1X PBS dilutions of feces in LB agar plates supplemented with the appropriate antibiotics were incubated overnight and YFP, CFP or mCherry-labeled bacterial numbers were assessed by counting the fluorescent colonies using a fluorescent stereoscope (SteREO Lumar, Carl Zeiss). The detection limit for bacterial plating was ~300 CFU/g of feces9.

All mice (Mus musculus) used in this study were supplied by the Rodent Facility at Instituto Gulbenkian de Cincia (IGC) and were given ad libitum access to food (Rat and Mouse No.3 Breeding (Special Diets Services) and water. Mice were kept at 20-24C and 40-60% humidity with a 12-h light-dark cycle. For the in vivo evolution experiment we used the gut colonization model previously established9. Briefly, mice drank water with streptomycin (5g/L) only for 24h before a 4h starvation period of food and water. The animals were then inoculated by gavage with 100L of an E. coli bacterial suspension of ~108 colony-forming units (CFUs). Mice A2, B2, D2, E2, G2, H2 and I2 were successfully colonized with the invader E. coli, while mice C2 and F2 failed to be colonized. Six- to eight-week-old C57BL/6J non-littermate female mice were kept in individually ventilated cages under specified pathogen-free (SPF) barrier conditions at the IGC animal facility. Fecal pellets were collected during more than one year (>400 days) and stored in 15% glycerol at 80C for later analysis. In the competition experiments between the invader ancestral E. coli and evolved populations, we colonized the mice using a 1:1 ratio of each genotype, with bacterial loads being assessed and frozen on a daily basis after gavage.

In vivo competition experiments in which the two modes of selection (directional and diversifying) were acting for a longer time period were performed using evolved invader E. coli populations colonizing mice D2, B2 and A2, H2. Here we used both male (n=8) and female (n=8) C57BL/6J mice aged six- to eight-week-old treated with streptomycin during 3 days before gavage. E. coli populations evolving for short time periods do not allow for strong conclusions on which mode of selection is taking place. Evolved invader populations such as I2 or G2 were therefore not used for in vivo fitness assays. To assess the impact of the mouse resident E. coli in the competitive fitness of dgoR we performed one-to-one competitions between the invader ancestral and dgoR KO clones. We first homogenized the mice microbiotas by co-housing the animals during seven days. The animals (n=6, female C57BL/6J mice aged six- to eight-week-old) were then maintained under co-housing and given streptomycin-supplemented (5g/L) water during seven days to break colonization resistance and eradicate their resident E. coli. At this point, the co-housed mice were removed from the antibiotic-supplemented water for two days. The following day, one group of mice was gavaged with an mCherry-expressing resident E. coli (n=3 mice) while the other group (n=3) was not, with all animals being individually caged from this point on and receiving normal water without antibiotic. The day after gavage, all mice were colonized with a mix (1:1) of the invader ancestral and the dgoR KO clones, and the bacterial loads were assessed and frozen on a daily basis.

Fecal DNA was extracted with a QIAamp DNA Stool MiniKit (Qiagen), according to the manufacturers instructions and with an additional step of mechanical disruption32. 16S rRNA gene amplification and sequencing was carried out at the Gene Expression Unit from Instituto Gulbenkian de Cincia, following the service protocol. For each sample, the V4 region of the 16S rRNA gene was amplified in triplicate, using the primer pair F515/R806, under the following PCR cycling conditions: 94C for 3min, 35 cycles of 94C for 60s, 50C for 60s, and 72C for 105s, with an extension step of 72C for 10min. Samples were then pair-end sequenced on an Illumina MiSeq Benchtop Sequencer, following Illumina recommendations. Sampling for microbiota analysis was performed until the microbiota composition stabilized (~1 year after the antibiotic perturbation).

QIIME2 version 2017.1133 was used to analyze the 16S rRNA sequences by following the authors online tutorials (https://docs.qiime2.org/2017.11/tutorials/). Briefly, the demultiplexed sequences were filtered using the denoise-single command of DADA2 version 1.1434, and forward and reverse sequences were trimmed in the position in which the 25th percentiles quality score got below 20. Diversity analysis was performed following the QIIME2 tutorial35. Beta diversity distances were calculated through Unweighted Unifrac36. For taxonomic analysis, OTU were picked by assigning operational taxonomic units at 97% similarity against the Greengenes database version 13 (Greengenes 13_8 99% OTUs (250bp, V4 region 515F/806R))37.

DNA was extracted38 from E. coli populations (mixture of>1000 clones) or a single clone growing in LB plates supplemented with antibiotic to avoid contamination. DNA concentration and purity were quantified using Qubit and NanoDrop, respectively. The DNA library construction and sequencing were carried out by the IGC genomics facility using the Illumina Miseq platform. Processing of raw reads and variants analysis was based on the previous work39. Briefly, sequencing adapters were removed using fastp version 0.20.040 and raw reads were trimmed bidirectionally by 4bp window sizes across which an average base quality of 20 was required to be retained. Further retention of reads required a minimum length of 100bps per read containing at least 50% base pairs with phred scores at or above 20. BBsplit (part of BBMap version 38.9)41 was used to remove likely contaminating reads as explained previously39. Separate reference genomes were used for the alignment of invader (K-12 (substrain MG1655; Accession Number: NC_000913.2)) and resident (Accession Number: SAMN15163749) E. coli genomes. Alignments were performed via three alignment approaches: BWA-sampe version 0.7.1742, MOSAIK version 2.743, and Breseq version 0.35.144,45. Final average alignment depths for invader and resident populations across time points equalled 302 (median=236) and 253 (median=235), respectively. While Breseq provides variant analysis in addition to alignment, other variant calling approaches were used to identify putative variation in the sequenced genomes, and to verify data from Breseq. A nave pipeline39 using the mpileup utility within SAMtools version 1.946 and a custom script written in python was employed. Only reads with a minimum mapping quality of 20 were considered for analysis, and variant calling was limited to bases with call qualities of at least 30. At these positions, a minimum of 5 quality reads had to support a putative variant on both strands (with strand bias, pos. strand / neg. strand, above 0.2 or below 5) for further consideration. Finally, mutations were retained if detected in more than one of the alignment approaches, and if they reached a minimum frequency of 5% at a minimum of one time point sampled. Further simple and complex small variants were considered from freebayes version 0.9.2147 with similar thresholds, while insertion sequence movements and other mobile element activity was inferred via is mapper version 248 and panISa version 0.1.649, as well as Breseq, as previously described39. All putative variants were verified manually in IGV version 2.750,51. Raw sequencing reads were deposited in the sequence read archive under bioproject PRJNA666769. Population dynamics of lineage-specific dynamics and the resulting Muller plots were inferred manually and are meant strictly as a means of presenting the data. In order to generate these plots, mutations were sorted by frequency (descending for each time point at which the population was sampled). The largest frequency mutations were considered major lineages within which minor frequency mutations occurred. Assuming that a mutation, which arises subsequent to a preexisting mutation (an already differentiated lineage) cannot exceed the frequency of that preexisting mutation at any point, and will fluctuate in frequency with the preexisting one, we assigned mutations to the lineages within each population. While this resolved the majority of high frequency and medium frequency mutations, low-frequency mutations within the Muller plots cannot be placed with high confidence, and are only included for completeness.

To calculate the maximum prophage induction rate we grew E. coli lysogenic clones, starting with the same initial OD600 values: ~0.1 (Bioscreen C system, Oy Growth Curves Ab Ltd), with agitation at 37C in LB medium in the presence or absence of mitomycin C along time (5g/mL)9. The OD600 values were normalized by dividing the ones in the presence of mitomycin C by the ones in the absence of mitomycin C (sampling interval: 30min). The LN of this ratios along time originates a lysis curve, where the maximum slope corresponds to the maximal prophage induction rate for each clone analyzed. We tested evolved clones from mouse A2, H2 and G2 against the ancestral clone which only carries the Nef and the KingRac prophages. We also tested clones of the resident strain that had evolved in the presence of the invader for more than 400 days (these clones were sampled from mouse A2).

To calculate the maximum bacterial growth rate, we grew E. coli lysogenic clones, starting with the same initial OD600 values: ~0.1 (Bioscreen C system, Oy Growth Curves Ab Ltd), with agitation at 37C in LB medium along time using reading intervals of 30min. The LN of the OD600 values along time originates a growth curve, where the maximum slope corresponds to the maximum bacterial growth rate for each clone analyzed.

To test for metabolic differences of the psuK/fruA mutation, growth curves of evolved lysogenic E. coli clones, bearing the Nef and KingRac prophages, with or without the psuK/fruA mutation were performed with the same initial OD600 value (~0.03) for each clone. The clones were grown in glucose (0.4%) minimal medium (MM9-SIGMA) with or without pseudouridine (80 M) and absorbance values were obtained using the Bioscreen C apparatus during 12h.

Frozen stocks of E. coli clones were used to seed tubes with 5mL of liquid LB. These were incubated overnight at 37C under static conditions to assess the formation of cell flocks/clumps, observable to the naked eye, in order to evaluate the formation of cell aggregates. Biofilm was tested according a previously published protocol52 and to evaluate the motility capacity we adapted the protocol from Croze and colleagues53. Briefly, overnight E. coli clonal cultures grown with agitation at 37C in 5mL LB medium supplemented with streptomycin (100ug/mL) were adjusted to the same absorbance and a 3uL volume was dropped on top of soft agar (0.25%). Plates were incubated at 37C and photos were taken at day 1, 2 and 5 post-inoculation to assess swarming motility phenotype.

To estimate the number of generations of E. coli in the mouse gut, we used a previously described protocol to measure the fluorescent intensity of a probe specific to E. coli 23S rRNA (as a measure of ribosomal content) that correlates with the growth rate of the bacterial cells54. We measured the number of generations of the ancestral E. coli clone while colonizing the gut of 2 mice, treated during 24h with streptomycin (5g/L) before gavage, during 25 days.

Plasmid DNA was extracted from overnight cultures using a Plasmid Mini Kit (Qiagen), according to the manufacturers guidelines. Specific primers for the amplification of repA and repB genes, were used to determine the frequency of the 68935bp (~69Kb) and 108557bp (~109Kb) plasmids, respectively, in the invader E. coli population.

The primers used for repA gene were:

repA-Forward: 5-CAGTCCCCTAAAGAATCGCCCC-3 and repA-Reverse: 5-TGACCAGGAGCGGCACAATCGC-3.

For repB the primer sequences were:

repB-Forward: 5-GTGGATAAGTCGTCCGGTGAGC-3 and repB-Reverse: 5-GTTCAAACAGGCGGGGATCGGC3.

PCR amplification of plasmid-specific genes was performed in 12 isolated random clones from mouse A2 at days 104 and 493. PCR reactions were performed in a total volume of 25L, containing 1L of plasmid DNA, 1X Taq polymerase buffer, 200M dNTPs, 0.2M of each primer and 1.25U Taq polymerase. PCR reaction conditions: 95C for 3min, followed by 35 cycles of 95C for 30s, 65C for 30s and 72C for 30s, finalizing with 5min at 72C. DNA was visualized on a 2% agarose gel stained with GelRed and run at 160V for 60min.

P1 transduction was used to construct a dgoR mutant (dgoR KO). This KO strain was created by replacing the wild-type dgoR in the invader ancestral YFP-expressing genetic background by the respective knock-out from the KEIO collection, strain JW562755, in which the dgoR sequence is replaced by a kanamycin resistance cassette. The presence of the cassette was confirmed by PCR using primers dgoK-F: GCGATGTAGCGAGCTGTC, and yidX-R: GGGAATAAACCGGCAGCC. PCR reactions were performed in a total volume of 25L, containing 1L of DNA, 1X Taq polymerase buffer, 200M dNTPs, 0.2M of each primer and 1.25U Taq polymerase. PCR reaction conditions: 95C for 3min, followed by 35 cycles of 95C for 30s, 65C for 30s and 72C for 30s, finalizing with 5min at 72C. DNA was visualized in a 2% agarose gel stained with GelRed and run at 160V for 60min.

The Qiagen RNeasy Mini Kit was used for RNA extraction. RNA concentration and quality were evaluated in the Nanodrop 2000 and by gel-electrophoresis. DNase treatment was performed with the RQ1 DNase (Promega) by adding 0.5l of DNase to 1g of RNA and 1l buffer in a final volume of 15ul, followed by incubation 30min at 37C. Afterwards, 1ul of stop solution was added and incubation for 15min at 65C was performed to inactivate the DNase. As a control for complete DNA digest a PCR was performed on the reactions including positive controls. Reverse transcription was performed with M-MLV RT[-H] (Promega) by mixing 1g of RNA with 0.5l random primers (Promega) and nuclease free water to a volume of 15l, incubation at 70C for 5min and a quick cool down on ice. Afterwards the reverse transcription was accomplished by adding 5l of RT buffer, 0.5l RT enzyme and 2l dNTP mix, followed by incubation for 10min at 25C, 50min at 50C and 10min at 70C. The resulting cDNA was diluted 100-fold in nuclease free water before changes in gene expression were detected using the The QuantStudio 7Flex (Applied Biosystems) with iTaq Universal SYBR Green Supermix (BioRad) and the following cycling protocol: Hold stage: 2min at 50C, 10min at 95C. PCR stage (40 cycles): 15s at 95C, 30s at 58C, 30s at 60C. Melt curve stage: 15s at 95C, 1min at 50C then increments of 0.05C/s until 95C. Melt curve analysis was performed to verify product homogeneity. All reactions included six biological and three technical replicates for each sample. A relative quantification method of analysis with normalization against the endogenous control rrsA and employing the primer specific efficiencies was used according to the Pfaffl method (add reference). The primers used were designed with PrimerQuest (idt). The used primer sequences were: psuK - TGCGTTAGCAGCGATTGA, AATTTACGCCTGGTGGAGTAG; arcA - GATTCATGGTACGGGACAGTAG, CCGTGACAACGAAGTCGATAA; yjtD - CGCACATGGATCTGGTGATA, GGCGTGGCGTAGTAATGATA and rrsR - GTCAGCTCGTGTTGTGAAATG, CCCACCTTCCTCCAGTTTATC.

Correlation between microbiota diversity measures and E. coli loads (CFU) or persistence (1-presence or 0-absence) was performed in R using the statistical package rmcorr (version 0.5.2)56 and lme4 (version 1.1-10)57, respectively. The rate of accumulation of new ISs in vivo was compared using Wilcoxon paired signed ranked test for expected and observed insertions, while the rate of selective sweeps correlation was performed using the Spearman Correlation test. Selective sweeps were taken to be mutations or HGT events that reached>95% frequency in the population and kept high frequency until the end of the colonization. Statistical analysis of prophage induction as well as biofilm levels was performed using the Mann-Whitney test in GraphPad Prism (version 8.4.3). A single sample T-Test was used test if the growth rate of evolved invader clones deviates from the mean of the ancestral. A Wilcoxon rank sum test with continuity correction was used to compare the relative expression levels of the evolved clones with the ancestral. P values of<0.05 were considered significant.

Pearson correlation tests between the frequency and the change in frequency of a mutation were performed to search for evidence of negative frequency-dependent selection. These were conducted for every mutation that showed parallelism and for each mouse, provided that the mutation was detected in at least four time points. The correlations were calculated in R with cor.test, used for the association between paired samples.

Linear mixed models (R package nlme, v3.158) were used to analyze the temporal dynamics of the dgoR KO mutant frequency in the presence or absence of the resident E. coli. The frequency of the dgoR KO mutant was log10 transformed to meet the assumptions of parametric statistics.

Sample size in animal experiments was chosen according to institutional directives and in accordance with the guiding principles underpinning humane use of animals in scientific research. No data were excluded from the analysis. The experiments were randomized with animals being assigned arbitrarily to each experiment. The investigators were not blind towards the animal experiments.

The following statistics are designed to infer the prevalent type of selection from time-resolved mutant frequency data. Specifically, we use such data to discriminate adaptive evolution under directional selection, which can take place by periodic sweeps or by clonal interference, and adaptation under diversifying selection (to be defined below).

We use two test statistics for frequency trajectories of established mutants (i.e., mutants that have overcome genetic drift):

the frequency propagator G(x), defined as the probability that a trajectory reaches frequency x,

the sojourn time T(x), defined as the time between origination at a threshold frequency x0 and the first occurrence at frequency x, averaged over all trajectories reaching frequency x. In terms of the underlying coalescent, T(x) is the time to the last common ancestor for a genetic clade of frequency x.

These observables discriminate the following modes of adaptive evolution:

Periodic selective sweeps under uniform directional selection. This mode is characteristic of simple adaptive processes in small populations or populations with low mutational inputs, where adaptive mutations are rare enough to fix independently59,60,61. Almost all established adaptive mutations reach fixation, and sojourn times to an intermediate frequency x>x0 are of order of their inverse selection coefficient (up to logarithmic corrections):

$${{{{{rm{G}}}}}}left(xright)approx 1,{{{{{rm{T}}}}}}left(xright)sim frac{1}{s}.$$

(1)

Clonal interference under uniform directional selection. This mode occurs in asexual populations when adaptive mutations become frequent enough to interfere with one another59,60,61. Only a fraction of the established adaptive mutations reaches fixation; sojourn times to intermediate frequencies are set by a global coalescence rate (widetilde{sigma }) that is higher than the typical selection coefficient of individual mutations62:

$${{{{{rm{G}}}}}}left(xright) < 1,{{{{{rm{T}}}}}}left(xright)sim frac{1}{widetilde{sigma}}.$$

(2)

Details of these dynamics depend on the spectrum of selection coefficients and on the overall mutation rate, which set the strength of clonal interference. For moderate interference, where a few concurrent beneficial mutations compete for fixation, we expect a roughly exponential drop of the frequency propagator, (Gleft(xright)sim {{exp }}left(-lambda xright)), reflecting the probability that a trajectory reaches frequency x without interference by a stronger competing clade. Moderate interference generates an effective neutrality for weaker beneficial mutations and at higher frequencies63. This regime has been mapped for influenza64. In the asymptotic regime of a travelling fitness wave, where many beneficial mutations are simultaneously present, the fate of a mutation is settled in the range of small frequencies; that is, at the tip of the wave65. In this regime, emergent neutrality affects the vast majority of beneficial mutations and most of the frequency regime66. Hence, the frequency propagator rapidly drops to its asymptotic value (Gleft(x=1right)ll 1.)

Adaptation under diversifying selection. More complex selection scenarios involve selection within and between ecotypes, i.e., subpopulations occupying distinct ecological niches67,68. An important factor generating niches and ecotypes is the differential use of food and other environmental resources. In this mode, ecotype-specific, conditionally beneficial mutations reach intermediate frequencies after a time given by their within-ecotype selection coefficients, but fixation can be slowed down or suppressed by diversifying (negative frequency-dependent) cross-ecotype selection18,

$${{{{{rm{G}}}}}}left(xright)approx 1,{{{{{rm{T}}}}}}left(xright)sim frac{1}{s},left(xlesssim ,frac{1}{2}right)$$

(3)

$${{{{{rm{G}}}}}}left(xright) < 1,{{{{{rm{T}}}}}}left(xright)gg frac{1}{s}left(xto 1right).$$

(4)

The details depend on the details of the eco-evolutionary model (synergistic vs. antagonistic interactions, carrying capacities, amount of resource competition vs. explicitly frequency-dependent selection). In a model with directional selection within ecotypes, conditionally beneficial mutations rapidly fix within ecotypes, but lead only to finite shifts of the ecotype frequencies. In the simplest case, the resulting dynamics of ecotype frequencies is diffusive, resulting in an effectively neutral turnover of ecotypes18. Given negative frequency-dependent selection between ecotypes, fixations become even rarer and can be completely suppressed; that is, ecotypes can become stable on the time scales of observation. The separation of time and selection scales between intra- and cross-ecotype frequency changes is expected to be a robust feature of ecotype-dependent selection: sojourn of adaptive alleles to intermediate frequencies is fast, fixation is slower and rarer. In other words, ecotype-dependent selection is characterized by two regimes of coalescence times T(x).

Frequency propagators and the coalescence time spectra expected under these evolutionary modes are qualitatively sketched in Supplementary Fig.11. For periodic sweeps under directional selection (dark green, left column), G(x) depends weakly on x and T(x) is set by rapid sweeps for all x. For clonal interference under directional selection (green, center column), G(x) decreases substantially with increasing x and T(x) becomes uniformly shorter. Under negative frequency-dependent selection (brown, right column), G(x) decreases substantially with increasing x, while T(x) substantially increases for large x and diverges in case of strong frequency-dependent selection generating stable ecotypes (dashed lines). (see Supplementary Fig.11 for the results of simulations assuming a model of direction selection or assuming a resource competition model where ecotype formation occurs31.

This test is based on qualitative characteristics of the functions G(x), T(x) and does not depend on details of the evolutionary process. We evaluate G(x) and T(x) for host-specific families of frequency trajectories; sojourn times are counted from an initial frequency x0=0.01. Origination times at this frequency are inferred by backward extrapolation of the first observed trajectory segment; the reported results are robust under variations of the threshold x0 and the extrapolation procedure. We then compute two summary statistics: the probability (p) that a mutation established at an intermediate frequency xm reaches near-fixation at a frequency xf,

$$p=frac{{{{{{rm{G}}}}}}({x}_{f})}{{{{{{rm{G}}}}}}({x}_{m})},$$

(5)

and the corresponding fraction of sojourn times,

$$tau=,frac{{{{{{rm{T}}}}}}({x}_{f})}{{{{{{rm{T}}}}}}({x}_{m})}.$$

(6)

Here we use xm=0.3 and xf=0.95 to limit the uncertainties of empirical trajectories at low and high frequency; however, the selection test is robust under variation of these frequencies. We find evidence for different modes of evolution:

The long-term frequency trajectories of mice B2, D2 and E2 are consistent with predominantly frequency-dependent selection (Fig.2, Fig.4ac). The propagator G(x) is a strongly decreasing function of x, resulting in fixation probabilities (p)<0.5. Sojourn times T(x) show two regimes with a stronger increase in the frequency range x>0.6, as measured by time ratios >3.

The trajectories of mice A2, G2, and I2 show a signature of recurrent selective sweeps and clonal interference under uniform directional selection (Fig.4ac). The propagator G(x) is a decreasing function of x, resulting in fixation probabilities (p=0.2-0.8), depending on the strength of clonal interference. Fixation times are short, giving time ratios (tau lesssim 2).

The shorter trajectory of mouse H2 signals periodic sweeps under uniform directional selection (Fig.3, Fig.4ac). The origination rate of mutations is lower than in the longer trajectories, and G(x) shows a weak decrease with (p=1.) Sojourn times T(x) are short and grow uniformly with x, resulting in a time ratio =2.25. This pattern is expected under directional selection in the low mutation regime: (Tleft(xright)={{log }}left[x/(1-x)right]/{s}) for individual mutations with a uniform selection coefficient s, leading to =2.0 for xm=0.3 and xf=0.95 (this value is marked as a dashed line in Fig.4c).

The trajectories of non-mutator lines in the long-term in vitro evolution experiment of Good et al1, evaluated over the first 7500 generations, show an overall signal of clonal interference under uniform directional selection (Fig.4c, Supplementary Fig.12). The frequency propagators G(x) are strongly decreasing functions of x and sojourn times T(x) grow uniformly with x. We find (p=0.2-0.8) and (tau lesssim 2), similar to the pattern in mice A2, G2, and I2.

The code for selection tests from the mutation frequency trajectories can be found in theSupplementary Information file.

Further information on research design is available in theNature Research Reporting Summary linked to this article.

Continued here:

Two modes of evolution shape bacterial strain diversity in the mammalian gut for thousands of generations - Nature.com

Gundam Evolution apparently features secret MVP intros, complete with a fake out to draw maximum salt from players.

Fans have begun to discover these secret clips, which override your currently equipped MVP choice- not unlike Sombras Play of the Game intro from Overwatch.

Honestly the MVP fakeouts are great and I love the references they put in the animations, hope it doesnt get taken away, writes Twitter user SayaDoesStuff.

Even in the MVP screens, we aint safe from a Barbatos airdrop, writes YouTube user animegx45, warning people about the likelihood of being jumped by the Barbatos Gundam.

While no official explanation has been given for them, the leading theory is that it requires two Gundam Evolution players to score extremely close to each other- earning you the fake out MVP intro.

Some speculate that theyre tied to specific pairings of mobile suits- but given the Turn A Gundam has nothing to do with the DOM Trooper, its less likely compared to just being about closely-tied scores.

Weve reached out to Bandai Namco Entertainment about the conditions of the secret fake out MVPs in Gundam Evolution, and will update with their statement.

Many of these are also incredibly fun references to the Gundam series overall, too: the DOM Troopers perform a Jet Stream attack, complete with two additional DOMs to support you in taking down your opponent.

Some of the lesser mobile suits are sure to be satisfying for players too, such as the Guntanks which features it running over its adversaries before pointing its main guns to their head.

Considering that Gundam Evolution is built around the idea of esports, the idea of having fake outs like this is sure to be great for the community- just the fact that so many people are discussing how hype they are is a good sign for the community.

Continued here:

Gundam Evolution Has Secret Fake Out MVP Intros And The Internet Loves Them - GamerBraves

You can put lipstick on a pig, but its still a pig. Thats how the saying goes, and it only seems like yesterday we were in the era of green and black screens when the arrival of the internet triggered record levels of investment in the technology market searching for the definitive killer app with a modern look and feel interface for users.

Looking back at the user experiences of past technology systems compared to today can fill you both with nostalgia and horror, passing through relatively short-lived technology innovations of client/server and thin client along the way. Spending vast amounts of money just to make something look good without the underlying functionality was and still is fools gold, as well discover.

Early origins back to the 80s

The battle for desktop supremacy and the user experience has its origins in the early 1980s and the Apple vs Microsoft wars. Then the user experience was tightly integrated into the operating system and, in Apples case, the hardware, hence the focus was more on overall functionality than just look and feel.

For those who appreciate the beauty of innovation, the idea was encapsulated in the much-quoted Steve Jobs principle of design: Youve got to start with the customer experience and work back towards the technology not the other way around. This ensured Apple in many ways had the jump on Microsoft regarding delivery of the end-user experience, although the opportunistic and entrepreneurial flair of Bill Gates ensured Microsoft Windows ultimately won and monopolised the Battle of the Desktop. The rest, as we know, is history.

Technology should exist to make our lives easier and for the better. Accessibility and enabling capability to allow us to eschew mundane repetitive activity and maximise our time is the very essence of the digital experience. Great technology-enabled experiences always empower the user.

SaaS cloud applications continue to make quantum leaps in an era of design thinking where we are constantly reinventing work through the employee experience. This is done with the knowledge that everything now is about creating immersive digital experiences for customers and employees alike, which cross an enterprise architecture and the functional silos found in organisations and become reflected in ERP modular system design.

Dawn of the SaaS cloud era the power of the user experience

At the start of the SaaS cloud era for enterprise applications, circa 2011-2019, the clear industry leader was Workday. The Pleasanton, California-based brand undertook a considerable amount of investment to ensure its human capital management (HCM) offering was highly intuitive, easy to use and provided an attractive experience.

Adopting the belief that sex sells, Workday had the jump on Oracle in terms of look and feel, and quickly used this to capture market share and build product adoption momentum. Just to keep things balanced, one should note that SAP were nowhere to be seen, despite the siloed HCM functionality within the ERP giants moderately successful stand-alone offering, SuccessFactors.

The SaaS cloud era saw the user interface look and feel in the buyers mind as the superior requirement over underlying system functionality. Organisation buyers were, surprisingly or not, prepared to spend significant sums of money on something that looked pretty. After all, comparing early SaaS applications accessibility and usability against on-premise applications was an easy win, and this became the major recurring theme in the market.

Subsequently, all vendors increased R&D expenditure and focussed on the user experience, with simplification and ease of use being the constant drivers.

The extremes vendors went to was significant, like the example of retina eye scanning, recording how a user sees the screen and accesses functionality, then using the outcomes to reconfigure the system accordingly.

However, even with all the advances in user experience, the platform vendors were still locked into a suite of silo applications. People, regardless of whether being a customer or employee, knowingly and unknowingly touch many front of house and middle office systems to access a service.

Changing the conversation systems of experience

With technology innovation moving so fast, the days of debating the field position on a screen, list of values, and how pretty the web browser screen is, for the true pioneers of the experience economy, are now far behind us. The buying criteria of early SaaS systems is no longer the sole overriding factor.

The dawn of the #experienceeconomy has seen the rise of the experience platforms, first touted around 2019, with the likes of Salesforce and ServiceNow leading the charge and taking the game to the ERP vendors.

Whilst such systems can easily co-exist, naturally the competitive nature of the industry has seen these platforms perceived as taking ERP market share. Experience platforms are not ERP systems, and whilst there are some grey areas, on the whole they can digitalise work in the way work is actually executed inside an organisation, and not necessarily reflecting the out of the box, siloed functional SaaS processes.

Equally the industry approach has evolved allowing multi-channel accessibility, noting most users would rather access technology via their mobile devices. There exists now a focus on creating holistic systems of experience, where personalised services are accessible based upon role and individual whilst residing and leveraging standardised business processes as and when necessary.

To combat this, the ERP vendors are having to move into the new era, most notably Oracles announcement of OracleME earlier this year, which in my view is a direct response to the advance of pure experience platform vendors. It does demonstrate that the large ERP vendors are not immune to market forces; in this case Oracle just so happens to be the first to positively respond.

Drivers of #experienceeconomy

The global pandemic has accelerated the adoption of digital technology, and whilst the public sector appears to remain in the dark ages regarding the implementation of back-office systems, those in the commercial private sector that did not embrace or could not change fast enough simply went out of business, unable to weather the storm.

With the technology industry always taking note of consumer behaviour and demand, the following factors are driving continuous investment in creating experiences:

Consumer need for greater user adoption systems must be intuitive and easy to use by default.

Simplification of process recognising many operational business processes cut across an organisation and that people do not work in operational silos.

Continuous need for insight the use of real-time data analytics to drive the experience is key.

The ability to deliver flexible, intuitive, easy to use applications that reflect the way people engage and deliver a service by creating data driven immersive experiences accessible at a given point in time continues to attract significant investment monies.

Where are we heading?

The divide between humans and machines comes ever closer with the advancement of technology, specifically with artificial intelligence and machine learning. Therefore, the natural question to ask is, just where is all this heading?

Looking now at the advances of technology in terms of virtual reality and avatars, and the predicted greater adoption of Google Glass, what we are seeing is the creation of an even more accessible and immersive experience.

Welcome to the #experienceeconomy its not going away, folks!

Mark Sweeny, founder, de Novo Solutions

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From lipstick on a pig to the experience economy, the evolution of UX ERP Today - ERP Today

Humans all share a common African ancestry, making African history everyones history. Yet little is known about the genetic evolution of people living on the continent in the distant past.

Thanks to advances in genome sequencing technology, scientists are now able to compare the DNA of people alive today with DNA extracted from very old skeletons, giving us a unique snapshot of life in Africa from many thousands of years ago.

In the field of human genetics, the story of Mother Eve is a familiar one. It describes how all living humans descend from one woman who lived in Africa 200,000 to 300,000 years ago.

Evidence comes from studies of mitochondrial DNA (mtDNA) a segment of genetic material found in the human cell. Amongst other things, it permits the study of relatedness in populations. Because only mothers pass it down, it reveals the direct evolutionary line between a person living today and their most distant female ancestor.

But like most simple stories, the tale of mitochondrial Eve is neither entirely accurate nor complete. While scientists agree that the dawn of humans did indeed occur in Africa, Eve would have been one of many human females living at the time, and she would not have been the first.

Unfortunately, the reality is that mtDNA gives us limited insight into the timelines, or the patterns, of population spread and dispersal.

Molecular biologist Dr Mateja Hajdinjak explains the significance of this knowledge gap. African population history has shaped the world we all live in, so until we can reconstruct the events from Africas past, going back thousands of years, we cant fully understand how modern humans emerged.

Dr Hajdinjak is the post-doctoral researcher on the ORIGIN project, an EU-funded research initiative based at the Francis Crick Institute in London, UK that is analyzing DNA from human remains found in archaeological sites in Africa.

The goal of ORIGIN is to reconstruct African prehistory using ancient DNA analysis.

The information yielded from these DNA samples is being studied alongside the findings of the projects archaeologists, palaeontologists and museum curators.

Dr Hajdinjak is among a growing number of researchers working hard to fill in the historical blanks by moving beyond analysis of mtDNA to use the latest techniques in whole genome sequencing. This allows researchers to compare the DNA of people living today with DNA extracted from very old skeletons.

One of our basic questions is, how can we use ancient DNA to reconstruct past population migrations within Africa and between Africa and other parts of the world? said Dr Hajdinjak.

She adds that little is known about the past genomic landscape across Africa, as much of the genetic change occurred on the continent when some groups shifted from their hunter-gatherer way of life to become agriculturalists between 3,000 and 7,000 years ago.

By comparing past genomes, we can see how different human groups are interconnected, and how migrations happened at different times in history. Migrations allow people to mix and reproduce with new groups, which changes human biology over time.

A lot is already known about ancient European history thanks to modern sequencing techniques, but ancient DNA studies of African samples have lagged behind. The reason for this is that DNA degrades over time, and especially in the hot and humid climates that prevail in Africa.

However, thanks to cutting-edge genome enrichment tools that allow DNA from the tiniest fragments of bone or teeth to be extracted and then amplified, scientists are starting to make good progress sequencing ancient DNA from Africa too.

By studying the data in this way, the researchers are starting to reconstruct events from the distant past and to probe the relationships that emerged between different African populations.

The aim of ORIGIN is not simply to satisfy our natural curiosity about where we came from, but also to unravel the timeline of our genetic evolution, and to use this information to predict how we might evolve into the future.

Some genetic mutations will have been instantly beneficial to our African ancestors, and will have persisted through the gene pool to this day, thousands of years after they first arose. A key example is lactase persistence the ability to digest milk into adulthood.

Milk and milk products are a valuable source of energy, yet the default ancestral state is lactose intolerance. For adults living in early African farming communities, the ability to convert milk from their herds into glucose may have given them an evolutionary advantage over their lactose intolerant neighbors.

Another genetic variant that would have boosted human survival when it first emerged is the sickle cell mutation. This genetic variant confers a degree of protection against malaria.

However, the mutation is something of a double-edged sword, as it is also responsible for sickle cell disease a serious and life-long condition that is prevalent in parts of Africa to this day.

It would be very important to reconstruct how sickle cell mutations first appeared and spread, said Dr Pontus Skoglund, supervisor of the ORIGIN project.

By understanding when mutations happened and how they spread, we can better understand how humans respond to evolutionary challenges, said Skoglund.

Researchers involved in the EU-backed AfricanNeo project are particularly intrigued by early farming practices in Africa. They are comparing samples of ancient DNA with contemporary DNA to refine their understanding of when African populations started migrating across their continent.

These migrations had a huge impact on the genetic mixing of groups, but the researchers are finding that this expansion was a complex series of events that cannot be encapsulated into a neat mitochondrial Eve-style narrative.

Expansion was not uniform across the continent, said Associate Professor Carina Schlebusch. She is an evolutionary biologist at the University of Uppsala in Sweden and principal investigator of the project.

Some hunter-gatherer groups were replaced by farmers, she said, referring to the likelihood that conflict would have arisen between populations wanting to occupy the same land, and that farmers would have enjoyed a competitive edge over hunter gatherers. Other groups interacted and exchanged genes, and others still remained isolated for far longer than you might expect.

Its clear why we should all care about these complex events from Africas distant past, according to Dr Schlebusch.

History tends to repeat itself, she said. These past migratory events may well play a role in how we behave in our future. For example, climate change means there is likely to be more pressures on people who are forced to leave their homes. There is a chance there will be more conflicts between populations and that some minority groups will be replaced.

The more we learn about our history, she said. The more we can predict how things will work out in the future.

The research in this article was funded via the EUs European Research Council and the Marie Skodowska-Curie Actions (MSCA). The article was originally published on Horizon, the EU Research & Innovation magazine.

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How prehistoric DNA is helping to unlock the secrets of human evolution - EL PAS USA