Monthly Archives: March 2024

Colin Firth and Matthew Macfadyen as Mr Darcy in the 1995 TV series and 2005 film adaptations of Pride & Prejudice. (BBC/United International Pictures)

Pride and Prejudice, for many, is the pinnacle of romantic literature, but Jane Austen's creation has also served as a key turning point on screen too: the evolution of the female gaze.

The 'female gaze' refers to a number of things in theory, for one it's the representation of the viewpoint of women both on and off screen ie female directors, writers, and viewers. It can also represent the connection that an audience has with the story, especially if it emphasises the idea of a woman being desired and the idea of a man being the object of desire.

The tale of Elizabeth Bennet and Mr Darcy's romance is ripe ground for the depiction of the female gaze on the silver screen, something that is well represented by the 1995 TV show when Colin Firth's Mr Darcy walked around in a wet shirt after a swim in a lake (which has now been sold at auction for 20,000).

Joe Wright's 2005 film also provided plenty of scenarios that made viewers swoon, and have since become significant cultural moments in their own right. Matthew Macfadyen might be best known for his award-winning role in Succession for some, but for many women it is his depiction of Mr Darcy that stands out thanks to his line delivery, his longing gazes at Keira Knightley's Elizabeth, and of course his infamous hand flex.

These moments all represent the female gaze in some way, the 1995 TV series shows Firth in a desirable fashion, while Macfadyen's depiction of Mr Darcy oozes with attraction the moment he crosses paths with Knightley's strong-willed Elizabeth. In both scenarios female viewers can imagine themselves in Elizabeth's shoes, as the desirer and the desired.

Since then this notion of the female gaze has only grown stronger as time has gone on. Just look at the success of Netflix's Bridgerton to see how successful romance can be on the silver screen the show, like Austen's work and its depictions, focuses on love stories that highlight the woman being courted more so than the men they're involved with.

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Bridgerton even pays homage to Pride & Prejudice in its second season, when Jonathan Bailey's Anthony falls into a lake and is left completely soaked, allowing his romantic interest Kate Sharma (Simone Ashley) to pine for him because of his state of undress.

Romance novels and period dramas have also given way to Byronic heroes, men who have gloomy personalities but are capable of strong passion towards their romantic interest. Characters like Heathcliff in Emily Bront's Wuthering Heights serve as an apt example of this, and these kinds of characters are also ones that serve the female gaze well in cinema.

Shows like Poldark and Outlander have made actors Aidan Turner and Sam Heughan stars overnight because of their depictions of romantic heroes. While chick flicks have also long been used as a way to represent the female gaze over the years.

It's not just films and dramas aimed at women that have highlighted the female gaze, it can also happen in those predominantly aimed at men the James Bond franchise for one. 2006's Casino Royale famously features a scene in which Daniel Craig's 007 emerges from the ocean in slow-motion. It's a scene that feels tailor-made to entice female viewers. For a movie franchise with a long history of objectifying women, it was a refreshing change of pace.

Desire and desirability aside, films focused on women also represent the female gaze onscreen. Greta Gerwig's hugely successful blockbuster Barbie shared the plight that all women face in a patriarchal society with wit and wisdom unexpected for a film about a doll.

It quickly became a cultural phenomenon and earned more than 1bn at the box office, while for some this might make it seem like films about toys are the future the real takeaway should be this movies about women and for women are worth telling.

1995's Pride & Prejudice is streaming on BBC iPlayer.

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Pride & Prejudice and the evolution of the female gaze on screen - Yahoo News UK

Joe Wongs musical evolution has taken him from the streets of Milwaukee to composing the music for the Netflix series The Russian Doll to having legendary drummer Jim Keltner play on his album,Mere Survival, which was released in February.

Wongs podcast The Trap Set began with a focus on drummers but has expanded to include singers, guitarists, bassists, keyboardists and harpists. The series has aired over 300 episodesconversations reallywith Wong using music as a springboard to detour into the backroads that make creative people tick.

The blurb on Wongs new album reads, Wong finds wonder in the muck, spinning rare beauty into outsized songs of redemption and reconciliation.Chock full of tasteful Zombies-inspired orchestrated pop, standout track Ordinary Light suggests a collaboration of Harry Nilsson and Beach Boys. He cites Love and The Walker Brothers as favorites as well.

Wong took time recently to chat. He broached the task of taking his band on the road. The challenge with me is that I have 20 people in my band (laughs), the drummer is also in Pearl Jam, the guitar player Mary Timony has a busy career, the harpist Mary Lattimore has a busy career. So, we have to plan stuff pretty far out. But Ive tried to figure out how to put together different bands for different types of shows, so I dont need to play with the same folks or as many folks.

The first time he sang was at the end of 2019; Wong had finished his first album, but it hadnt been released. I wanted to play a show to see how the songs translated live. After soundcheck I was sitting behind the drums, which belonged to Matt Cameron, I was feeling comfortable and then remembered I wasnt going to be playing drums and that I had to go out and sing.

Wong says he got so nervous he blacked out and doesnt recall much of the show other than it felt really good. I think my conscious mind had to just shut down and let my subconscious drive the bus.

In the time since, Wong has gotten more comfortable as a singer, some of it from rehearsing for tours that kept getting cancelled during Covid; not to mention the thousands of shows he has played as a drummer. He says the nervousness before each show is a healthy sign that he is pushing himself into a period of growth.

With over 300 episodes and counting, it is safe to say Wong has engaged some legendary musicians. He relates to them on a personal level as well as a musical one.

He began the podcast about a decade ago because he was touring and not feeling the visceral joy he associated with playing music. I was trying to figure out what was going on and thought maybe I was stuck in a loveless marriage with music.

Listening to podcasts instead of music, he was drawn to the longform detailed conversations. He used Marc Marons podcast as a template. The feeling Wong had lost re-emerged and importantly, he figured out a lot of people go through similar periods of ennui.

He has spoken to genre-defining geniuses, but Wongs own brilliance was the initial focus on drummers. Drummers are often the least interviewed members of any band. Even if they are great. There is a lot of untapped gold there.

He said the Keltner recording session came about when the legendary drummer, who had been a podcast guest, offered his services. Wong didnt have anything written but he thought it would be stupidnotto write songs for him to play on. So, I wrote a couple songs with him in mind.

The session was booked at the studio owned by Foo Fighters. It was easy get other musicians to play on that session because everyone wants to play with Jim. That session was recorded live to tape to capture Keltners innate groove.

Back to his idea for the podcast, Wong says he wanted to strike a balance between Marons freewheeling non-agenda and the preparedness of Terry Gross, host of NPRs Fresh Air. I was desperate to know the information I was asking people anyway. As a fan and music geek he gets to ask questions many of us have itched to delve into.

Wongs day job is writing music for film and television. Over the 20 years he has gotten comfortable staring at a blank screen knowing something will come by the time it is due.

When he began working on material for his first album, 2021sNite Creatures, he struggled with drowning in his own neuroses and couldnt finish anything.

Before the era of smart phones, he would call his own answering machine to leave ideas. Writing both albums was a blend of spontaneous ideas but also setting aside time every day to sit and let something happen.

He began using the tools he learned as a composer and applied them to his songwriting. Even if that was just five or 10 minutes in the morning. You can write a song in five minutesor at least the basic idea of the song. The person who writes the song and the person who decides to develop, and record it are two different versions of yourself. The editor is one aspect, and the creator is another aspect of yourself.

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Joe Wong's Musical Evolution - Shepherd Express

Genome selection and pangenome generation

We based our analysis on the proGenomes v.2.2 dataset containing 82,400 genomes grouped into 11,562 species (that is, specI clusters) that were defined based on 40 single-copy marker genes20. The corresponding species tree generated based on concatenated marker gene sequences was kindly provided by the authors of the proGenomes article20.

From this initial selection, we filtered out metagenome-assembled genomes, single-amplified genomes, genomes flagged as chimeric by GUNC39, genomes that were not taxonomically cohesive with the rest of the specI cluster according to GTDB26, genomes with no 16S rRNA gene sequence detected and genomes we could not confidently map to the MicrobeAtlas database (see Mapping genomes to MicrobeAtlas database OTUs below). The species tree was pruned to remove these genomes using the ETE Toolkit v.3 (ref. 40). As a result, we obtained 78,315 genomes grouped into 8,790 species. For each species, a pangenome was generated by clustering all gene sequences on 95% nucleotide sequence identity as described in ref. 41.

All gene sequences were clustered using MMseqs2 (ref. 42) with minimum overlap of 50%, minimum identity threshold of 80% and clustering mode 0. The rest of the parameters were left as default. For each gene cluster, whenever sequences originated from more than one genome within a species, we only retained sequences that were most similar to those from other species within the gene cluster. We then proceeded with gene clusters containing sequences from at least five different species. Sequences were then aligned using the automatic strategy selection option in MAFFT v.7.471 (ref. 43), with all other parameters left as default. On the basis of the multiple sequence alignment, a gene tree was generated using FastTree v.2.1.11 (ref. 44) using the generalised time-reversible model45 of nucleotide evolution, with all parameters left as default.

Before performing tree reconciliation, we subsampled the species tree using ETE Toolkit v.3 (ref. 40) to decrease computational requirements in the following manner: for each gene cluster, the species tree node corresponding to the last common ancestor of all species within the gene cluster was selected. Clades within the species tree not containing any genes from the gene cluster were collapsed for computational efficiency. Subsequently, the subsampled species tree was used to root the gene tree using the OptRoot module from RANGER-DTL v.2.0 (ref. 23). We then ran RANGER-DTL with default settings to perform gene and species tree reconciliation for a total of 500. Gene clusters in which more than 50 optimal roots were detected were not considered further. Reconciliations from each optimal root were aggregated using the AggregateRanger_recipient module from RANGER-DTL v.2.0. We used a custom script to aggregate results across optimal roots and detect tree nodes that were labelled as transfers. For downstream analysis, we considered only transfer events detected in 80% reconciliations that contained gene pairs with 0.5 minimum branch support in the gene tree. In addition, all multifurcations containing 100% identical genes from different species were considered to be transfer events.

For each genome, we counted a gene as having undergone transfer as long as its pangenome-representative gene was involved in a transfer event. For the denominator (that is, total number of genes assessed), we only considered genes if their pangenome-representative genes had passed all steps described above in HGT event detection. The number of genes transferred was then divided by the total number of genes assessed and the average based on all genomes within a species was calculated. For the examples mentioned in the main text, we used data from specI_v3_Cluster259 for A. baumannii and data from specI_v3_Cluster712 for L. monocytogenes.

The NCBI Sequence Read Archive46 was searched for samples and studies containing any of the keywords metagenomic, microb*, bacteria or archaea in their metadata. The corresponding raw sequence data (as of 7 March 2020) were downloaded and quality filtered. To assign OTU labels, quality filtered data were mapped to MAPref v.2.2.1 using MAPseq v.1.0 at a 0.5 confidence level47. We then filtered out samples containing less than 1,000 reads and/or less than 20 OTUs defined at 97% 16S rRNA gene identity and retained samples with at least 90% community coverage (calculated based on the formula in ref. 48).

NCBI Sequence Read Archive sample metadata were parsed to classify every sample into four general environments: animal, aquatic, plant and soil. Subsequently, we calculated BrayCurtis distances between all samples in the dataset and compared community compositions in samples from independent studies. When a sample was consistently similar to samples assigned to a different environment, we adjusted its environment label. In cases where samples with similar community compositions had no general agreement between assigned environments, we removed the environmental label.

We used barrnap49 with default settings to predict 16S rRNA gene sequences in the genome selection, proceeding with sequences of 50% of expected length. The sequences were then mapped to MAPref v.2.2.1 using MAPseq v.1.0 (ref. 47), retaining only sequences that mapped to an OTU with a 0.3 confidence level. Genomes containing multiple 16S rRNA gene copies were mapped to OTUs based on a majority rule (50% copies) or high confidence (at least one copy with a 0.98 confidence level). Species containing multiple genomes were mapped to OTUs based on majority (50% genomes).

For each OTU within the dataset, the average abundance was calculated separately for all samples assigned to the animal, aquatic, plant and soil environments. The OTU was then assigned to its preferred environment based on the highest of the four numbers.

Distances between gene and species pairs were extracted from the corresponding trees using the dist function in ETE Toolkit v.3 (ref. 40). To plot the distribution in Fig. 2a, only gene pairs with 0.5 minimum branch support values and 50% sequence overlap within the multiple sequence alignment were considered. Gene pairs with and without transfer events were normalized with respect to species distance by splitting the species distance distributions into 80 bins and subsampling the group with the larger number of pairs in each bin (either transfer detected or no transfer detected) to the number of pairs in the second group in the corresponding bin (either no transfer detected or transfer detected). The same procedure was performed for normalizing gene pairs with and without transfer events with respect to gene distance. After normalization, the resulting distributions were compared using the two-sided MannWhitney U-test.

To calculate gene ubiquity, we counted the number of genomes represented by a gene in each pangenome versus the total number of genomes in the species. For subsequent analysis, only species encompassing ten or more genomes were considered. We used previously defined thresholds25 to distinguish extended core genes (90% gene ubiquity) and cloud genes (15% gene ubiquity). In the species pair participating in HGT, the species with the higher gene ubiquity was labelled as the putative donor, whereas the species with the lower gene ubiquity was labelled as the putative recipient. To compare extended core and cloud genes with or without transfer events, a two-sided Fishers exact test was performed.

We used the COG category and KEGG pathway functional annotations provided by the proGenomes database after running eggNOG-mapper for eggNOG 5.0 (ref. 35). Each gene cluster was annotated to the corresponding functional categories based on the union of all gene annotations within the cluster. To analyse genes associated with the mobilome, we looked up which terms corresponded to the XMobilome: phages, transposons category in the database of COGs50,51 (mobilome, curated, in Extended Data Fig. 4). In addition, we extracted terms that contained the following keywords in the annotations provided by the proGenomes database: phage, transposon, transposase, transposition, transposable, mobile, mobilization, integrase, integration, plasmid, conjugative, conjugation, transformation and competence (mobilome, uncurated, in Extended Data Fig. 4). To analyse genes associated with transcription regulation, we extracted terms from the transcription category that contained the following keywords in the annotations provided by the proGenomes database: regulation and regulator (transcription regulation, uncurated, in Extended Data Fig. 4). We calculated a functional categorys background expectation fraction by counting the total number of genes that passed the pipeline that were annotated to this category divided by the total number of genes that passed the pipeline.

For each detected transfer event, we calculated the average species and gene distance by taking all average pairwise distances between left descendants and right descendants of the transfer event (for gene distance calculations, only gene pairs with 50% sequence overlap were considered). The resulting distribution of species and gene distances can be seen in Fig. 2e. For functional enrichment analysis, minimum and maximum species and gene distance cut-offs were selected in such a way that there were no bins without observations, with the resulting area divided into thirds. We also looked specifically at transfer events at the 0.01 and 0.05 gene distance cut-offs (approximately 99% and 95% sequence identity, respectively) as these results would be more comparable to previous studies that detected HGT events based on nearly identical sequences. We then counted the number of transfer events annotated to each functional category divided by the total number of transfer events in the area. The observed fraction of events annotated to a specific function was then tested with a two-sided binomial test against the fraction of all genes on which the pipeline was run that were annotated to this function. Resulting P values were corrected for multiple testing using the HolmSidak method.

A similar procedure was performed using KEGG ortholog annotations, grouping them into KEGG pathway maps (0910109145) for Extended Data Fig. 5 and antimicrobial resistance genes (BR:ko01504) for Extended Data Fig. 6.

We compared our functional enrichment analysis results with those from refs. 9,13,18,28,29,30,31,32,33,34. In most of these studies, functional categories were based on the COG database, with the exception of ref. 13 (with categories based on the SEED52) and refs. 9,28 (both with categories based on TIGRFAMs53). The mapping between COG categories and KEGG pathways (used in our study), SEED and TIGRFAMs can be found in Supplementary Table 1.

For our study, we considered enrichment data from the most recent transfers, that is, gene distance bins 0.000.01, 0.000.05 and 0.000.25. These three gene distance bins together with three species distance bins provided us with nine data points to consider for each functional category. We assigned a functional category to have strong evidence for enrichment or depletion in transfers if at least seven of the nine data points showed significant enrichment or depletion. We assigned a functional category to have weak evidence for enrichment or depletion in transfers if most data points showed enrichment or depletion but this was not always statistically significant.

For ref. 18, we considered the results depicted in Fig. 8d and Supplementary Table 13 of the article. We calculated the first and third quartiles of the HGT index using all genes in Supplementary Table 13. We assigned a functional category to have strong evidence for enrichment in transfers if the median HGT index from genes in this category was greater than the third quartile. We assigned a functional category to have strong evidence for depletion in transfers if the median HGT index from genes in this category was less than the first quartile. Only functional categories containing at least five genes were considered.

For ref. 34, we considered the results depicted in Fig. 9 of the article. We considered only recent HGT events (99% nucleotide sequence identity). We assigned a functional category to have strong evidence for enrichment in transfers if the median recent HGTs in this category was greater than the third quartile. We assigned a functional category to have strong evidence for depletion in transfers if the median recent HGTs in this category was less than the first quartile.

For ref. 32, we considered the results depicted in Fig. 4a (HTgenes row) of the article. We considered a functional category to have strong evidence for enrichment or depletion in transfers if the observed-to-expected ratio of orthologous groups was significantly different from one.

For ref. 31, we considered the results depicted in Supplementary Fig. 7 of the article. We considered a functional category to have strong evidence for enrichment or depletion in transfers if the relative proportion of transferred genes was significantly over- or underrepresented when compared with the set of all bacterial genes.

For ref. 30, we considered the results depicted in the first two columns of Table 3 of the article. We considered a functional category to be enriched in transfers if its relative transferability was higher than one, and to be depleted in transfers if its relative transferability was lower than one. We used a P value cut-off of 0.05 to distinguish strong and weak evidence for enrichment or depletion.

For ref. 33, we considered the results depicted in Table 2 of the article. In the table, functional categories were listed that significantly differed from the background of all gene families. We used a P value cut-off of 0.05 to distinguish strong and weak evidence for enrichment or depletion.

For ref. 29, we considered the results depicted in Fig. 4b of the article. We used Z-score cut-offs of 2 and 2 to distinguish strong and weak evidence for enrichment or depletion.

For ref. 13, we considered the results depicted in Supplementary File 6 (SEED level 1 and SEED level 2) of the article. We used a P value cut-off of 0.05 to distinguish strong and weak evidence for enrichment or depletion. We downweighted depletion evidence for the transcription (regulatory) and signal transduction categories as they both mapped to regulation and cell signalling in the SEED. For COG categories that mapped to multiple categories in the SEED, we indicated evidence based on the consensus from these categories.

For ref. 28, we considered the results depicted in Table 2 of the article. We downweighted depletion evidence for cell cycle control and mitosis and cell motility as they both mapped to the cellular processes in TIGRFAMs. We also downweighted enrichment evidence for carbohydrate transport and metabolism as there was no one-to-one mapping for this category.

For ref. 9, we considered the results depicted in Fig. 2 of the article. We considered a functional category to be enriched in transfers if the proportion of transferred genes was greater than 10%, and to be depleted in transfers if the proportion of transferred genes was less than 3%.

An OTU was detected as present in a given sample if its relative abundance was at least 0.01%. To calculate the co-occurrence between two OTUs, we counted the number of samples in which both OTUs were present and divided it by the number of samples in which the less prevalent OTU was present. Phylogenetic distances between OTUs were retrieved from the MicrobeAtlas database 16S rRNA tree using the dist function in ETE Toolkit v.3 (ref. 40).

For modelling the relationship between co-occurrence and phylogenetic distance, we only considered OTUs that exchanged at least 1 gene with 30 other OTUs and OTU pairs in which both OTUs were present in at least 20 environmental samples. The power law equation (1) is as follows:

$${{rm{CO}} approx ktimes {rm{PD}}^{a},}$$

(1)

where CO stands for co-occurrence, PD stands for phylogenetic distance, and k and a are parameters fitted using the nlstools package in R54. Model residuals were then used to calculate Spearman correlations with the number of genes transferred. To generate the background distribution, the number of genes was shuffled before calculating Spearman correlations. The resulting distributions of Spearman correlations generated based on raw co-occurrence (precorrection), model residuals (postcorrection) or background were compared with each other using the two-sided MannWhitney U-test.

The analysis depicted in Fig. 4bd has been performed using a similar set-up as described in Gene and species distance normalization. We used the 7 genes transferred cut-off to denote OTU pairs with many transfer events as this corresponded to the 80% quantile of OTU pairs with at least 1 gene transferred.

Global networks of predicted interactions were computed with FlashWeave v.0.19.0 (ref. 38). This method uses the local-to-global learning approach55 to learn the skeleton of a Bayesian network encoding putative ecological relationships between species adjusted for ecological or technical confounders. To this end, FlashWeave uses an interleaved testing scheme that (1) heuristically determines likely confounding variables for each pair of species (based on univariate associations and previous iterations of the algorithm), and (2) subsequently tests whether the focal association holds when conditioned on these candidate confounders.

The parameters used for running FlashWeave were as follows: sensitive=false, heterogeneous=true, and max_k=3 (with confounder correction) or max_k=0 (without confounder correction). With these settings, FlashWeave converts non-zero read counts to centred log-ratio-transformed values to account for compositionality and discretizes these values. Mutual information tests are then run on the discretized values. We used co-occurrence data from all 95,422 OTUs contained within the environmental sample dataset, filtering the resulting network for edges between the 4,380 OTUs for which transfer event data were generated. OTU pairs with a score higher than zero were considered as interacting. To normalize for differences in phylogenetic distance and co-occurrence distributions between species with at least seven genes transferred and species with zero or one gene transfer, the procedure described in Gene and species distance normalization was performed with simultaneous subsampling on phylogenetic distance and co-occurrence for 8080 bins.

We used the same relative abundance numbers as calculated in Preferred habitat assignment. For each OTU, we only considered its abundance within its preferred environment, denoting high-abundance OTUs as those whose abundance was above the 80% quantile in this environment. In contrast, we denoted low-abundance OTUs as those whose abundance was below the 20% quantile in this environment. OTU pairs were then sorted based on phylogenetic distance and the fraction of OTU pairs with at least one transfer event detected was calculated for each phylogenetic distance bin. Error bands were calculated using Bernoullis principle of uncertainty. Resulting fractions were then pairwise compared between the highhigh, highlow and lowlow groups using a one-sided Wilcoxon rank-sum test. Resulting P values were corrected for multiple testing using the BenjaminiHochberg method.

We computed a generalism index for each OTU reflecting its environmental flexibility. This index was calculated based on the entropy of the OTUs abundance values across the four major environments (animal, aquatic, soil and plant). OTUs with similar abundances across environments had higher entropy. OTUs with uneven abundances across environments (a higher abundance in one or a few of the environments compared with the rest) had lower entropy.

To compare inter-environmental transfers, we selected 200 OTUs assigned to each environment (see Preferred habitat assignment) that displayed the highest entropy (generalists) and 200 OTUs that displayed the lowest entropy (specialists). OTU pairs were then subsampled in such a way that phylogenetic distance distributions were equal between all environments and between generalists, specialists and all species. We then counted the fraction of OTU pairs with at least one transfer event detected. To generate the background expectation, OTU pairs from all species were subsampled to the target phylogenetic distance distribution 1,000. We then fit a normal distribution to the generated data using the fitdistr function in R56 to get an estimate of the expected mean, s.d. and range of transfer rates between different environments.

Data from Figs. 2 and 4b,c and Extended Data Figs. 16 were visualized using seaborn v.0.11.2 (ref. 57) and matplotlib v.3.5.1 (ref. 58) in Python v.3.7.4. Data from Figs. 3, 4a, and 5 and Extended Data Fig. 7 were visualized using ggplot2 v.3.3.5 (ref. 59) in R v.4.1.1.

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

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A global survey of prokaryotic genomes reveals the eco-evolutionary pressures driving horizontal gene transfer - Nature.com

All light-sensing structures on the chiton shell, Varney explained, are attached to nerves, which pass through the shell slits to connect to the bodys main nerves. The slits function as cable organizers, bundling sensory neurons together. More slits mean more openings through which nerves can run.

It so happens that the number of slits is standard information that is recorded anytime somebody describes a new chiton species. The information was out there, but without the context of a phylogeny to map it back to, it didnt have any meaning, Varney said. So I went and looked at this and started seeing this pattern.

Varney saw that twice, independently, lineages with 14 or more slits in the head plate evolved eyespots. And twice, independently, lineages with 10 or fewer slits evolved shell eyes. She realized that the number of slits locked into place which kind of eye type could evolve: A chiton with thousands of eyespots needs more slits, whereas a chiton with hundreds of shell eyes needs fewer. In short, the number of shell slits determined the evolution of the creatures visual systems.

The findings lead to a new set of questions. One that the researchers are actively investigating is why the number of slits constrains the type of eye that can evolve. Answering that will require work to elucidate the circuitry of the optic nerves and how they process signals from hundreds or thousands of eyes.

Alternatively, the relationship between eye type and the number of slits might be driven not by the needs of vision but by the way the plates develop and grow in different lineages, Sumner-Rooney suggested. Shell plates grow from the center outward by accretion, and eyes are added throughout the chitons life as the edge grows. The oldest eyes are those in the middle of the animal, and the most recently are added at the edges, Sumner-Rooney said. As a chiton, you might start life with 10 eyes and finish your life with 200 eyes.

As a consequence, the growing edge of a shell plate has to leave holes for new eyes many small holes for the eyespots, or fewer larger holes for the shell eyes. Too many or too-big holes could weaken a shell to its breaking point, so structural factors might limit which eyes are possible.

Much remains to be discovered about how chitons see the world, but in the meantime, their eyes are primed to become biologists new favorite example of path-dependent evolution, Nilsson said. Examples of path dependence that can be really well demonstrated, as this case [is], are rare even though the phenomenon is not only common, its the standard way things happen.

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Mollusk Eyes Reveal How Future Evolution Depends on the Past - Quanta Magazine

Before investigators can answer whether antibody-drug conjugates (ADCs) should become a frontline standard of care in advanced nonsmall cell lung cancer (NSCLC), researchers must first look to establish robust biomarkers of response and the exact mechanisms of action at play, according to Benjamin Levy, MD.

We have a lot of work to do to try to figure out how to leverage these drugs. We are in chapter 1 of whats going to be a very long novel about ADCs in NSCLC, Levy stated in an interview with OncLive.

In the interview, Levy discussed remaining questions regarding the utility of ADCs, expanded on the patient and disease characteristics that may render a patient eligible for treatment with an ADC, and highlighted ongoing and planned research within the treatment arena.Levy is the clinical director of Medical Oncology at Johns Hopkins Sidney Kimmel Cancer Center at Sibley Memorial Hospital, and an associate professor of oncology at Johns Hopkins University School of Medicine, in Baltimore, Maryland.

Levy: Weve come a long way in the field of ADCs, specifically for NSCLC. Of course, we have trastuzumab deruxtecan thats now approved for patients with HER2 exon 20 insertion mutations. However, thats just the beginning of the story. Were just beginning to scratch the surface.

Other ADCs are in development such as patritumab deruxtecan [HER3-DXd], a HER3 ADC thats being leveraged and looked at in EGFR-mutant lung cancer. There are others, such as MET-directed ADCs that look promising.

Of course, there are also the TROP2-directed ADCs. [Overall], there is a lot of movement in this field, and over the next 6 to 12 months were going see more and more data come out with how to use these drugs.

We only have a nascent understanding of how these ADCs work. On paper, the monoclonal antibody component of the ADC should target the cell surface protein on the cancer cell and then be internalized and release the payload. The story is a little bit more complicated than that.

Target engagement of a cell surface protein is one mechanism of action. However, we also have the bystander effect where payload moiety can move to adjacent cells once released, and kill those cells, even if they dont have the target. [There is also] antibody-dependent cellular cytotoxicity. These drugs may work not only by targeting cell surface antigens and releasing the payload, but also by engaging effector immune cells and bringing them into the tumor. [In that sense] we can view them as immunotherapy [agents]. Thats yet another way we think these drugs may work.

Its important [to note] that as of February 2024, the only approved ADC in NSCLC [was] trastuzumab deruxtecan, and its approved for patients with HER2 mutations. I want to take a step back because HER2 alterations come in many sizes, shapes, and forms. Theres HER2 overexpression, which is looked at routinely in breast cancer. Theres HER2 amplification, and then theres the HER2 mutation.

Keep in mind when youre looking at your next-generation sequencing report or your molecular report that the patient must have that [mutation] to receive trastuzumab deruxtecan. Notably, the drug is approved after chemotherapy and not in the first-line setting. We have more data coming out, [but trastuzumab deruxtecan] is certainly a great drug to use after youve exhausted first-line options.

We have a lot of work to do with biomarker selection and we have a lot of questions that are unanswered. The first is, are the IHC platforms that we currently have able to best predict the efficacy of these ADCs? Do we need other ways to look at IHC? I havent given up on IHC, but I think we need to look at it in a different way.

The second thing is that driver mutations may be the best predictor of ADC [efficacy], whether it be a HER2 exon 20 insertion, an ALK rearrangement, or an EGFR mutation. [Driver mutations] as we see it may be the best predictor of response to these drugs. A lot of work needs to happen here. We are just at the ground floor of understanding which patients are going to respond or who will develop toxicity. In addition, we need a tissue or a blood-based biomarker. It'll be very exciting to see over the next two years how things unfold.

I-DXd is an ADC with a monoclonal antibody and IgG1, a linker, and a deruxtecan derivative payload. The monoclonal antibody targets B7, which is a protein that is expressed on multiple malignancies, including NSCLC. There are very preliminary data coming out [with this agent].

A lot of the data for I-DXd came out of small cell lung cancer [SCLC], though were learning in small data sets that this drug is active in NSCLC, and some squamous cell populations. Were still learning about the ability to target B7H3, thats the name of the protein, very early on. However, this drug looks promising both in SCLC and in NSCLC, demonstrating meaningful activity in a highly pretreated group of patients.

There are a lot of questions about ADCs and how theyre going to unfold in NSCLC. The first question [has to do with] biomarker selection. We have a lot of work to do there. We now have a bona fide biomarker for trastuzumab deruxtecan with the HER2 exon 20 insertion. However, there a lot of questions about where these drugs are going to land in the treatment continuum. Are they going to be first-line [agents]? Are they going to be second-line [standards]? Are we going to be able to safely combine these drugs with immunotherapy and platinum?

Looking at [these agents] in the advanced stage setting [is vital], but [we must also consider] how these drugs perform in earlier-stage disease. Can we look at these agents in the neoadjuvant setting? Is there potential synergy with neoadjuvant immunotherapy? Some of these things are going to be played out in trials.

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Levy Delves Into the Evolution of ADCs in NSCLC - OncLive

Roughly 128 million years ago snakes suddenly burst into an abundant existence on Earth, eventually diversifying into the 4,000 or so species we see today.

Yet their prevalence can't be solely attributed to their most obvious characterizing traits: abandonment of limbs and body elongation. While 25 different groups of lizards are limbless, no other type of reptile has reached the explosive level of diversity seen among snakes.

To explain their success, Stony Brook University evolutionary macroecologist Pascal Title and colleagues examined the genetics and diets of more than 60,000 specimens of snakes and lizards from museums around the world to see what makes a noodle form of scaly life so successful.

"We found that snakes have been evolving faster than lizards in some important ways, and this speed of evolution has let them take advantage of new opportunities that other lizards could not," explains University of Michigan evolutionary biologist Daniel Rabosky.

Snakes seem to have hit an evolutionary jackpot a rapid pulse of successful biological innovation that allowed them to thrive in copious variations.

It's likely there were many driving factors, the researchers concede, but a shift in the way snakes feed separates them from other reptiles. This includes flexible skulls allowing them to swallow animals significantly larger than their heads and a highly sophisticated chemical detection system to find and track this prey.

"If there is an animal that can be eaten, it's likely that some snake, somewhere, has evolved the ability to eat it," says Rabosky.

The team's genetic analysis, including 1,018 species of snakes and lizards, found snakes were evolving up to three times faster than lizards, with multiple bursts of rapid evolution across history.

So when the asteroid took out non-avian dinosaurs about 66-million-years-ago, snakes not only had the biological tools to survive but the genetic capacity to rapidly adapt to the changing environments around them and take advantage of now-vacant niches.

From flying serpents to frog-goo-eaters, snakes now call every continent except Antarctica home. And despite their very straightforward body plan, snakes still manage to pack in a dazzling variety of traits and appearances.

"A standout aspect of snakes is how ecologically diverse they are: burrowing underground, living in freshwater, the ocean and almost every conceivable habitat on land," explains phylogeneticist Alexander Pyron from George Washington University ."While some lizards do some of these things there are many more snakes in most of these habitats in most places."

What exactly about their genetics gives snakes such a speedy evolutionary clock is currently a mystery.

But Earth may owe its incredible arrays of life to such sudden and dramatic events known as macroevolutionary singularities, when a perfect combination of unpredictable traits and circumstances click into place. The rapid emergence of flowering plants is another example, Title and team point out.

"What I love about this study is how it integrates hard-earned field and museum data with new genomic and analytical methods to show a basic biological truth: Snakes are exceptional and frankly quite cool," concludes California State University evolutionary biologist Sonal Singhal.

This research was published in Science.

Excerpt from:

The Snake Is The Spearhead of Reptile Evolution, But Why? - ScienceAlert

Some 510 finches from five species were released on Floreana Island as part of a nature restoration programme, according to Ecuadors Ministry of the Environment.

A flock of finches, the birds famously studied by Charles Darwin in his theory of evolution, have been reintroduced to an area of the Galapagos Islands.

Since 2023 experts have been working to eradicate introduced species which have caused the disappearance of numerous native species, paving the way for ecological restoration.

"This is a very special day, says Elicer Cruz, Spokesman for the Jocotoco Conservation Foundation.

Maintaining viable population percentages in case of unforeseen circumstances is the Floreana Projects most important mitigation measure.

Some 510 finches from five species were released on Floreana Island as part of a nature restoration programme, according to Ecuadors Ministry of the Environment.

Floreana is one of the 13 islands that make up the archipelago along with several islets.

Getting the birds ready for release was a "long and meticulous" process, which involved breeding and care in captivity. One of the five species, the critically endangered medium tree finch, "is only found on Floreana Island, nowhere else in the world," says Arturo Izurieta, director of the Galapagos National Park.

The birds were recovered and cared for by specialists and scientists in special aviaries built to observe them.

After several months of care - and the eradication of species that threatened them - the flock was freed on 26 February, according to the Environment Ministry.

Introduced species such as cats and rodents have caused the disappearance of more than a dozen endemic or native species, according to non-governmental conservation organisations.

The release of the finches is a concrete step towards ecological restoration and sustainability on Floreana Island.

The birds have been equipped with tracking devices to monitor their movements, ensuring their well-being and aiding ongoing research efforts.Drones will also be employed to monitor groups of up to 40 birds at a time, providing valuable insights into their behaviour and habitat usage.

Video editor Joanna Adhem

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'A very special day: Birds linked to Darwins theory of evolution reintroduced to Galapagos Islands - Euronews

Evolution is a slow process occurring over many generations, but it can happen more quickly in some cases than others. Everything from the type of animal or plant evolving to changes in the external environment can affect the pace of evolution. Now a team of scientists has found that why mitochondria, the powerhouses of cells, may be evolving rapidly in some plants.

Mitochondria contain their own independently inherited genomes, said Justin Havird, an assistant professor in integrative biology. In most animals, mitochondrial DNA mutates and evolves much faster than the DNA in the nucleus that encodes most of our genes. In plants the opposite pattern is found, though some plant groups show a more animal-like pattern with fast-evolving mitochondria. We wanted to find out why.

Kendra Zwonitzer, a doctoral student in Havirds lab and lead author of a new study published in the Proceedings of the National Academy of Sciences, found that this trait in plants is linked to having a low number of mitochondrial genome copies in each cell.Some of the plants, geraniums for example, have only around one mitochondrial genome per cell and evolve very quickly, while species with slower evolution have dozens or hundreds per cell.

We found that these plants with really low mitochondrial DNA copy numbers were, across the board, showing elevated evolution rates. This applied broadly across ~300 million years of plant evolution, Zwonitzer said.

The team believes this is due to repair machinery in plants. Plant mitochondria with damaged DNA need other copies of DNA close by for repair processes.

If theres only one or two copies floating around in the cell, maybe theres not enough copies around to perform that repair, Zwonitzer said. And thats why were seeing more mutations and an elevated evolution rate.

The researchers used data from DNA sequencing to analyze the number of copies of mitochondrial genes and nuclear genes in each plant. They then formed evolutionary trees to understand the evolutionary rate. The scientists hope to understand whether this pattern also applies to organisms outside of plants to better understand the link between mitochondrial genomes and repair in the future.

One of the questions is, Is this just something weird thats going on in plant mitochondrial genomes, or is this something more common and somewhat of a rule across the tree of life? Havird said. There are a lot of animals that are in the early branches of the tree of animal life, where we see some similar patterns that we see in plant mitochondrial genomes. This could be something thats found in other lineages, like octocorals and sponges.

The research was funded by the U.S. National Institute of General Medical Sciences.

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Why the Powerhouses of Cells Evolve Differently in Plants - College of Natural Sciences