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Lewis Hamilton has opened up about his 2021 world title heartache and his efforts to return Mercedes to competitive ways after a difficult start to the 2022 season.

Hamilton was on course for a record-breaking eighth world title at last year's Abu Dhabi Grand Prix when a late Safety Car, and controversial restart, turned the race on its head.

Running old, Hard tyres as the race restarted for one lap, Hamilton was overhauled by Red Bull rival Max Verstappen, who had pitted to switch to the Soft compound rubber.

Having reacted graciously to the defeat post-race, the Briton subsequently took a break from social media, returning shortly before Mercedes' 2022 car launch and pre-season testing.

Then, as the W13 took to the track, it quickly became clear that it would not be a front-running machine or one Hamilton could fight back with.

In an interview for Viaplay, Hamilton was asked about the 'double blow' of missing out on the 2021 title and returning in 2022 with an uncompetitive car.

"It was definitely tough," said Hamilton, speaking to former teammate Heikki Kovalainen.

"My winter was a lot of self-reflection... I was surrounded by my family, that was the most beautiful thing. My family really, really reacted and all came around me.

"I wasn't on my phone, I wasn't on social media... I was just playing with the kids, building snowmen. We were playing in the water, playing in the waves.

"I continued to train, because that was kind of healthy for my mindset, and a lot of thinking, kind of, 'What do I want to do next? How do I want to take this?'

"Then, to turn the negative into a positive and be like, 'Okay, I'm gonna come back and fight', and then to not have the ability to be able to fight back, and regain what I had fought for last year, has definitely been tough."

Kovalainen pointed out that Hamilton has never looked frustrated or desperate so far this season, prompting further self-reflection from the 37-year-old.

"I think it's definitely not been perfect. I've definitely not been perfect in the background," Hamilton commented.

"I definitely would say it's been a struggle, particularly off the end of last year, so it's been a real kind of odd kind of growth process.

"I've tried a lot of things with the car and experiments, and it's often caught us out. I've struggled with feeling comfortable in the car this year.

"To finally be in a place where that's not the case... I'm in a more kind of leading position with the car now, rather than it leading me. It's been good."

He added: "I think also there's people in our team that have been here for many, many years, even before our success, and then there's people that are new to the team, who have been here only with the wins, so this is a new experience for them also.

"I think for anybody, it's a good experience to have. The adversity only makes you stronger. It's the failures, I think, that truly make us strong."

Originally posted here:
Hamilton sheds fresh light on how he recovered from 'double blow' - Racingnews365.com

Theres only a week left until Saints Row releases. Yes, after almost a decade in hibernation, the high-flying tongue-in-cheek crime simulator is back with lengthy tales to tell as well.

On Monday, publisher Deep Silver released a fresh trailer for Saints Row that goes into the story. Set in a fictional city in the American southwest called Santo Ileso, this car-jacking adventure is about a group of up-and-comers looking to make a name for their gang, The Saints, as is the tradition in these games. Therell be rival organizations to take down and a villain thats rocking one mean mustache.

Watch the new Saints Row trailer for yourself below. There arent any glaring spoilers, so dont worry about seeing too much of the plot before launch. Plus, this isnt quite something driven by its narrative.

The humor is still intact, even if its nowhere near as silly as Saints Row 4 was. Yeah, its a shame that this one wont be about fighting-off aliens as the president of the United States again.

Saints Row releases Aug. 23, 2022, for PS4, PS5, Xbox One, Xbox Series X|S, and PC via the Epic Games Store. It was originally coming out In February but got hit with a six-month delay.

Written by Kyle Campbell on behalf of GLHF.

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Saints Row trailer sheds light on the story - For The Win

Photo: Billy Napier; Credit: Alex Shepherd

The biggest storylines at the University of Florida may be the positive momentum on the recruiting trail or the opening of the highly anticipated state-of-the-art football training facility, but not for long.

As the college football season rapidly approaches, programs around the nation continue to ramp up their practice sessions to simulate in-game reps to the best of their ability.

Intra-team scrimmages are an integral part of that process. The Gators had their first scrimmage of the fall on Saturday, where the coaches and players got a gauge of what aspects of the game plan are clicking and the shortcomings that will need extra attention in the coming three weeks.

Head coach Billy Napier spoke to the media on Monday about the session, shedding light on what he saw. But, mostsignificantly, he said he believes the team has taken significant strides from a fundamental standpoint from the spring, a primary focus of the new regime as they attempt to rid the self-inflicted wounds present a season ago.

"I thought both sides of the ball were taking steps forward," Napier said Monday. "The film was much cleaner than maybe spring practice. I think, fundamentally, we're a lot farther along. I do think the communication's improving."

However, despite the evident progression this fall, there is still a way to go before they're ready to line up against Utah in The Swamp week one, namely on the offensive side of the football.

"I think the offensive unit first offensive unit was the only team that didn't meet the quota, didn't meet the goal for the day. I think those are all very correctable if that makes sense. Procedural-related. Outside of that, I thought it was pretty clean."

While that may appear problematic at first glance, offenses are known to operate behind the curve in the preseason and early regular season as they attempt to gel. The moving pieces going into the offensive game plan including a new scheme, a new quarterback, a new play caller and more make it challenging to find that rhythm early on.

That continues even in the era of high-octane offense. As unideal as that may be. Florida is a victim of that reality.

Napier largely credited the defense for a portion of the offensive miscues.

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"I thought the defense did a good job of limiting explosive plays," Napier said.We threw it to the defense in a couple situations where it happens: third-and-long, two-minute, nothing alarming here. Its going to happen. The good thing is our defense is creating some of that.

"Were playing well, were affecting the quarterback, were playing well on the back end. Part of our formula here is weve got to protect and affect. Weve got to protect our quarterback and affect the other quarterback. You do that lots of different ways: you push the pocket, you tip balls, you hit the quarterback, you sack the quarterback, the element of disguise."

To make those needed adjustments and grow prepared for the top ten test on September 3, execution at the helm by starting quarterback Anthony Richardson is imperative, especially when the offense is put in a position to put points on the board.

"I think that we didn't score touchdowns in the red area. That was an objective that we didn't meet on offense. Overall, we did turn it over a few times. So, the quarterback efficiency wasn't what we wanted it to be. I think a lot of things contribute to that when you're playing with a mixture of lineups, different positions and different players. It can be better. That's what I would say."

Going forward, Florida is slated to continue building toward game-ready form.

Not only will that consist of further schematic and performance adjustments for players and coaches, but Napier looks to bring in college officials as they did for the first one for their second scrimmage next Saturday.

This allows the coaching staff to further evaluate the unit's glaring issues from discipline or functional errors prior to the year.

"All college officials that had a good set of eyes on everything, told them to call it just like it was a game day," Napier said regarding scrimmage one. "We wanted to know if we'd have issues. We wanted to expose those. Next Saturday, we've got a full SEC crew coming into town for practice Friday and also the scrimmage Saturday. Putting a premium on that. We're heading in the right direction there."

All in all, Florida believes it's making the necessary steps in the right direction, and are using the limited intra-squad competition to expedite the process of getting everyone in sync.

Stay tuned to AllGators for continuous coverage of Florida Gators football, basketball and recruiting. Follow along on social media at @SI_AllGators on Twitter and Florida Gators on Sports Illustrated on Facebook.

Original post:
Gators HC Billy Napier Sheds Light On Florida's First Scrimmage of the Fall - Sports Illustrated

Kirk Herbstreits son Zak Herbstreit followed in his fathers footsteps by committing to Ohio State out of high school. Now, the younger Herbstreit is a full-fledged member of the Buckeyes.

A walk-on tight end out of high school in 2021, Zak is entering his second season in the program. On Wednesday, he ditched his black stripe ahead of the 2022 season.

First I want to appreciate all of yall for getting me better, said Herbstreit. Means a lot to me. Shout out my unit, old guys in Cade (Stover), Mitch (Rossi). Love you guys.

Listed at 6-foot-2 and 243 lbs., Herbstreit is a part of a deep tight-end unit for Ohio State and likely faces a steep battle for playing time in 2022. Either way, he is now a full Buckeye, an honor that will stick with him regardless of how the season plays out.

Ohio online sports betting is officially launching on January 1, 2023. Ohio will join other Big Ten states where sports betting has become legalized such as Pennsylvania, Michigan, Illinois and more.

More:
Kirk Herbstreits son, walk-on TE at Ohio State, sheds black stripe - Saturday Tradition

GettyCole Custer works in the garage area at Michigan International Speedway.

There have been questions about Cole Custer throughout a 2022 season that has featured numerous struggles. Now Stewart-Haas Racing has provided some clarity about the 2020 Rookie of the Years NASCAR Cup Series future.

Greg Zipadelli, SHRs chief competition officer, met with media members ahead of the trip to Watkins Glen International. He addressed several topics, such as Kevin Harvicks recent winning streak, before facing a question about the driver of the No. 41 Ford Mustang.

Right now, I believe that is what our plan is right now. Yes, Zipadelli said about keeping Custer in the No. 41. Were just looking and trying to sort out the [No.] 10 car at this particular time.

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Obviously, the statement leaves some wiggle room for Stewart-Haas Racing. The answer indicates that Custer will return, but there could also be some changes. There will still be some questions about Custer and the status of the No. 41 until the organization makes an official announcement.

GettyCole Custer waits for qualifying at Phoenix Raceway.

One reason for the question about Custer is that SHR still has to announce plans for multiple entries. This includes the No. 41 that Custer has controlled since 2020 and the No. 10 that features Aric Almirola.

Another reason for the question is a situation over at another team. Joe Gibbs Racing and Kyle Busch have not been able to reach an agreement to bring the two-time champion back to the No. 18, primarily due to the lack of sponsorship.

Busch has acknowledged to reporters that he has spoken to other teams and created questions about possible destinations. SHR has stood out as an option considering the number of sponsors on hand, the possible openings in the building, and the quality of the equipment that heads to the track each week.

If Busch became available on the market, it would make sense if he moved over to SHR and took over one of the entries. He would automatically contend for wins in whichever entry he landed.

GettyCole Custers season has featured surprising issues.

A winner at Kentucky Speedway in 2020, Custer has not returned to Victory Lane in 2021 or 2022. He has posted four total top-10 finishes, but he has also dealt with numerous issues on the track.

The list includes a 20-lap run at Michigan International Speedway when Custer dealt with three flat tires. The final blown tire caught on fire and sent the California native back to his pit stall. His day came to an end while the crew extinguished the blaze.

The most recent race on the schedule, which took place at Richmond Raceway, featured Custer lining up inside of the top 10 for the opening pace laps. He continued to race in the top 10 before securing points at the end of Stage 1.

However, the situation drastically changed for Custer. He lost all power steering in the No. 41, so he had to contend with handling issues for the remainder of the 400-lap race. He ultimately ended the day 26th overall and three laps down after showing up to the track with a fast Ford Mustang.

READ NEXT: Ryan Blaney Secures Massive NASCAR Contract Extension

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Stewart-Haas Racing Sheds Light on Cole Custer's Future - Heavy.com

Shotgun sequencing and generation of metagenome-assembled genomes

In our previous study, 78 Holstein Friesian dairy cows were sampled for rumen content, metagenomic shotgun sequencing was carried out, and raw Illumina sequencing reads were assembled into contigs using megahit assembler using default settings [7]. We used a pooled assembly of the original 78 samples to increase the quality of the metagenome-assembled genomes (MAGs) with the syntax: megahit [14] -t 60 -m 0.5 1 [Illumina R1 files] 2 [Illumina R2 files]. Next, the assembled contigs were indexed using BBMap [15]: bbmap.sh threads=60 ref=[contigs filename]. Thereafter, reads from each sample were mapped to the assembled contigs using BBTools bbwrap.sh script. In order to determine the depth (coverage) of each contig within each sample, the gi_summarize_bam_contig_depths tool was applied with the parameters: gi_summarize_bam_contig_depths --outputDepth depth.txt --pairedContigs paired.txt *.bam --outputDepth depth.txt --pairedContigs paired.txt.

Using the depth information, metabat2 [16] was executed to bind genes together into reconstructed genomes, with parameters: metabat2 -t40 -a depth.txt.

To evaluate genomic bin quality, we used the CheckM [17] tool, with parameters: checkm lineage_wf [in directory] [out directory] -x faa --genes -t10.

We generated 93 unique high-quality MAGs, and further increased our MAG database by including phyla that were not represented in our set of MAGs. In order to do so, we used the published compendium of 4,941 rumen metagenome-assembled genomes [18] and dereplicated those MAGs using dRep [19]. We then selected MAGs from phylum Spirochaetes, Actinomycetota, Proteobacteria, Firmicutes, Elusimicrobia, Bacillota, Fibrobacteres and Fusobacteria, which had the highest mean coverage in our samples as calculated using BBMap and gi_summarize_bam_contig_depths as described above [15]. This strategy minimized the false discovery rate (FDR), that would have been obtained if larger and unspecific databases would have been employed [20] and allowed the addition of 14 MAGs to our database.

In order to create the proteomic search library, genes were identified along the 107 MAGs using the Prodigal tool [21], with parameters: prodigal meta and translated in silico into proteins, using the same tool. Replicates sequences were removed. Protein sequences from the hosting animal (Bos taurus) and common contaminant protein sequences (64,701 in total) were added to the proteomic search library in order to avoid erroneous target protein identification originating from the host or common contaminants. Finally, in order to subsequently assess the percentage of false-positive identifications within the proteomic search [22], the proteomic search library sequences were reversed in order and served as a decoy database.

The bacterial fraction from rumen fluid of the 12 selected animals selected from extreme feed efficiency phenotypes, were obtained at the same time as the samples analyzed for metagenomics and stored at 20C until extraction. To extract total proteins, a modified protocol from Deusch and Seifert was used [23]. Briefly, cell pellets were resuspended in 100l in 50 mM Tris-HCl (pH 7.5; 0.1 mg/ml chloramphenicol; 1 mM phenylmethylsulfonyl fluoride (PMSF)) and incubated for 10min at 60C and 1200rpm in a thermo-mixer after addition of 150l 20mM Tris-HCl (pH 7.5; 2% sodium dodecyl sulfate (SDS)). After the addition of 500l DNAse buffer (20mM Tris-HCl pH 7.5; 0.1mg/ml MgCl2, 1mM PMSF, 1g/ml DNAse I), the cells were lysed by ultra-sonication (amplitude 5160%; cycle 0.5; 4 2min) on ice, incubated in the thermo-mixer (10min at 37C and 1,200rpm) and centrifuged at 10,000 g for 10min at 4C. The supernatant was collected and centrifuged again. The proteins in the supernatant were precipitated by adding 20% pre-cooled trichloroacetic acid (TCA; 20% v/v). After centrifugation (12,000 g; 30min; 4C), the protein pellets were washed twice in pre-cooled (20C) acetone (2 10min; 12,000 g; 4C) and dried by vacuum centrifugation. The protein pellet was resuspended in 2 SDS sample buffer (4% SDS (w/v); 20% glycerin (w/v); 100mM Tris-HCl pH 6.8; a pinch of bromophenol blue, 3.6% 2mercaptoethanol (v/v)) by 5min sonication bath and vortexing. Samples were incubated for 5min at 95C and separated by 1D SDS-PAGE (Criterion TG 4-20% Precast Midi Gel, BIO-RAD Laboratories, Inc., USA).

As previously described, after fixation and staining, each gel line was cut into 10 pieces, destained, desiccated, and rehydrated in trypsin [24]. The in-gel digest was performed by incubation overnight at 37C. Peptides were eluted with Aq. dest. by sonication for 15min The sample volume was reduced in a vacuum centrifuge.

Before MS analysis, the tryptic peptide mixture was loaded on an Easy-nLC II or Easy-nLC 1000 (Thermo Fisher Scientific, USA) system equipped with an in-house built 20cm column (inner diameter 100m; outer diameter 360m) filled with ReproSil-Pur 120 C18-AQ reversed-phase material (3m particles, Dr. Maisch GmbH, Germany). Peptides were eluted with a nonlinear 156min gradient from 1 to 99% solvent B (95% acetonitrile (v/v); 0.1% acetic acid (v/v)) in solvent A (0.1% acetic acid (v/v)) with a flow rate of 300ml/min and injected online into an LTQ Orbitrap Velos or Orbitrap Velos Pro (Thermo Fisher Scientific, USA). Overview scan at a resolution of 30,000 in the Orbitrap in a range of 300-2,000m/z was followed by 20 MS/MS fragment scans of the 20 most abundant precursor ions. Ions without detected charge state as well as singly charged ions were excluded from MS/MS analysis. Original raw spectra files were converted into the common mzXML format, in order to further process it in downstream analysis. The spectra file from each proteomic run of a given sample was searched against the protein search library, using the Comet [25] search engine with default settings.

The TPP pipeline (Trans Proteomic Pipeline) [26] was used to further process the Comet [25, 27] search results and produce a protein abundance table for each sample. In detail, PeptideProphet [28] was applied to validate peptide assignments, with filtering criteria set to probability of 0.001, accurate mass binning, non-parametric errors model (decoy model) and decoy hits reporting. In addition, iProphet [28, 29] was applied to refine peptide identifications coming from PeptideProphet. Finally, ProteinProphet [28,29,30] was applied to statistically validate peptide identifications at the protein level. This was carried out using the command: xinteract -N[my_sample_nick].pep.xml -THREADS=40 -p0.001 -l6 -PPM -OAPd -dREVERSE_ -ip [file1].pep.xml [file2].pep.xml.. [fileN].pep.xml>xinteract.out 2>xinteract.err. Then, TPP GUI was used in order to produce a protein table from the resulting ProtXML files (extension ipro.prot.xml).

Subsequently, proteins that had an identification probability < 0.9 were also removed as well as proteins supported with less than 2 unique peptides (see Supplementary Table1).

A reference database containing all 107 MAGs contigs was created (bbmap.sh command, default settings). Then, the paired-end short reads from each sample (FASTQ files) were mapped into the reference database (bbwrap.sh, default settings), producing alignment (SAM) files, which were converted into BAM format. Subsequently, a contig depth (coverage) table was produced using the command jgi_summarize_bam_contig_depths --outputDepth depth.txt --pairedContigs paired.txt *.bam. As each of the MAGs span on more than one contig, MAG depth in each sample was calculated as contig length weighted by the average depth. Finally, to account for unequal sequencing depth, each MAG depth was normalized to the number of short sequencing reads within the given sample.

In order to compare metagenomic and proteomic structures, we first calculated the mean coding gene abundance and mean production levels of each of the 1629 detected core proteins over all 12 cows. Both mean gene abundance and mean production level were translated into ranks using the R rank function. The produced proteins were ranked in descending order and the coding genes in the gene abundance vector were reordered accordingly. The two reordered ranked vectors then plotted using the R pheatmap function, and colored using the same color scale.

As our goal was to analyze plasticity in microbial protein production in varying environments, e.g., as a function of host state, only MAGs that were identified in all of the 12 proteomic samples were kept for further analysis. Consequently, only proteins that were identified in at least half of the proteomic samples (e.g., in at least six samples) were selected. This last step aimed to reduce spurious correlation results. These filtering steps retained 79 MAGs coding for a total of 1,629 measurable proteins.

In order to calculate the accuracy in predicting host feed efficiency state based on the different data layers available (16S rRNA (Supplementary Table2), metagenomics, metaproteomics), the principal component analysis (PCA) axes for all the samples based on the microbial protein production profiles were calculated. Then, twelve cycles of model building and prediction were made. Each time, the two first PCs of each of five cows along with their phenotype (efficiency state) were used to build a Support Vector Machine (SVM) [R caret package] prediction model and one sample was left out. The model was then used to perform subsequent prediction of the left-out animal phenotype (feed efficiency) by feeding the model with that animals first two PCs. This leave-one-out methodology was then repeated over all the samples. Finally, the prediction accuracy was determined as the percent of the cases where the correct label was assigned to the left-out sample. For the proteomics data, this procedure was applied on both the raw protein counts, and the protein production normalized based on MAG abundance, which enabled us to compare the prediction accuracies of the microbial protein production to that of the raw protein counts.

In order to split the proteomics dataset into microbial proteins that tend to be produced differently as a function of the host feed efficiency states, each microbial protein profile was correlated to the samples host feed efficiency measure (as calculated by RFI) using the Spearman correlation (R function cor), disregarding the p value. Proteins that had a positive correlation to RFI were grouped as inefficiency associated proteins. In contrast, proteins that presented a negative correlation to RFI were grouped as efficiency associated proteins. To test for equal sizes of these two protein groups, a binomial test was performed (R function binom.test) to examine the probability to get a low number of feed efficient proteins from the overall proteins under examination, when the expected probability was set to 0.5.

Protein functions were assigned based on the KEGG (Kegg Encyclopedia of Genes and Genomes) [31] database. The entire KEGG genes database was compiled into a Diamond [32] search library. Then, the selected microbial proteins were searched against the database using the Diamond search tool. Significant hits (evalue < 5e-5) were further analyzed to identify the corresponding KO (KEGG Ortholog number). Annotations of glycoside hydrolases were performed using dbcan2 [33].

The checkerboard distribution in protein production profiles was estimated separately within the feed efficient and inefficient animal groups. To enable the comparison between the two groups checkerboardness level, we chose a standardized C-score estimate (Standardized Effect Size C-score - S.E.S C-Score), based on the comparison of the observed C-score to a null-model distribution derived from simulations. The S.E.S C-score was estimated using the oecosimu function from R vegan package with 100,000 simulated null-model communities.

The functional redundancy within a given group of proteins was measured as the mean number of times a given KO occurred within a given group, while neglecting proteins that have not been assigned a KO level functional annotation.

In order to test whether a given group of proteins exhibits more or less functional redundancy than would have been expected, a null distribution for functional redundancy was created, based on the number of proteins in the given group. A random group of proteins was drawn from the entire set, keeping the same sample size as in the tested group, and the process was repeated 100 times. Then, the functional redundancy for each random protein group was calculated. Thereafter, the null distribution was used to obtain a p value to measure the likelihood of obtaining such a value under the null.

Examining the functional divergence between the two groups of proteins, e.g. the feed efficiency and inefficiency associated proteins, was done by first counting the amount of shared functional annotations, in terms of KOs between the two groups. Thereafter, a null distribution for the expected count of KOs was built by randomly splitting in an iterative manner the proteins into groups of the same sizes and calculating the number of shared KOs. A p value for the actual count of shared proteins was obtained by ranking the actual count over the null distribution.

ANN Ratio analysis was carried out independently for each protein function (KO), containing more than 14 proteins with at least 5 proteins within each feed efficiency group. Initially, all proteins assigned to a given KO were split into two sets, in accordance to their feed efficiency affiliation group. Thereafter, proteins within each set were independently projected into two-dimensional space by PCA applied directly to Sequence Matrix [34]. Average nearest neighbor ratio within each set was then calculated within the minimum enclosing rectangle defined by principal component axes PC1 and PC2, as defined by Clark and Evans [35].

Microorganism feed efficiency score was calculated for each MAG individually by first ranking each protein being produced by the given microbe along the 12 animals, based on the normalized protein production levels. Thereafter, a representative production value for the microbe in each animal was calculated as the average of the ranked (normalized) protein production levels in that animal (using R rank function). This ranking allowed us to alleviate the potential skewing effect of highly expressed proteins. The microorganisms Feed Efficiency Score was calculated as the difference between its mean representative production value within feed efficient animals to that within feed inefficient animals. Values close to zero will reflect similar distribution between the two animal groups, positive values will indicate higher expression among efficient animals, and negative values will indicate higher expression among inefficient animals. To calculate significance, the actual feed efficiency score was compared to values in a distribution derived from a permutation based null model. Each of the permuted Feed Efficiency Scores (10,000 for each microbe) was obtained by independently shuffling each of the proteins produced by the MAG between the animals, prior to calculating the actual microorganism feed efficiency score. By positioning the absolute score value over its distribution under permuted assumptions (absolute values), we obtained a significance p value.

In order to assess the link between phylogenetic similarity between the MAGs and their association with feed efficiency, phylogenetic tree estimating evolutionary relationships between the MAGs was constructed using the PhyloPhlAn pipeline [36]. The phylogenetic signal for Microorganism Feed Efficiency Score was estimated by providing the phylogSignal function from R phylosignal [37] package with MAGs phylogenetic tree and respective values. Pagels Lambda statistics was chosen for the analysis, owing to its robustness [38].

All bar plots, scatter plots and other point plots were generated with R package ggplot2. Heatmaps were produced by either ggplot2 [39] or pheatmap [https://cran.r-project.org/web/packages/pheatmap/index.html] R packages. KEGG map was produced using the online KEGG Mapper tool [40]. Phylocorrelogram was produced with phyloCorrelogram function from R package phylosignal [37].

MAGs that contain a minimal number of proteins (50 functions) were selected for differential protein production analysis, in order to have sufficient data to perform statistical tests. For each MAG, the relative production was used in order to calculate the Jaccard pairwise dissimilarity for core protein production between feed efficient and inefficient cows using the R vegan package. Analysis of similarity between efficiency and inefficiency associated proteins for each MAG (ANOSIM) values and p values were then calculated using the same package.

Using all GH annotated proteins, a feature table that sums the count of each GH family within each sample was produced. Thereafter a leave-one-out cross-validation (LOOCV) [R caret package] was performed, each time building a Random Forest (RF) prediction model from the GH family counts and efficiency state of 11 samples, leaving one sample outside. Each one of the RF models, in its turn, was applied on the left-out animal to predict its efficiency state. Model accuracy and AUC curve were calculated based on the LOOCV performance.

Read this article:
Metaproteome plasticity sheds light on the ecology of the rumen microbiome and its connection to host traits | The ISME Journal - Nature.com

Nothing recently dropped the advertised peak brightness of the phone (1) after it was discovered that the handset's screen is only capable of hitting 700 nits as it stands, but the company maintains that the original claim is correct.

The controversy stemmed from Nothing's advertising of 1,200 nits of peak brightness for the phone (1). However, a test performed by German tech site ComputerBase revealed that the Nothing phone (1) could not achieve a brightness higher than 700 nits despite efforts to create scenarios that would cause it to achieve its advertised rating.

Nothing clarifies that this is due to software limitations.

"The hardware is capable of reaching up to 1,200 nits peak brightness, but this is currently capped by the software to 700 nits," a company spokesperson told Android Central. "This decision was made to ensure a balanced user experience regarding heat and battery consumption."

To provide more context, Nothing explained that normal conditions permit a minimum brightness of 500 nits, though this relies on the environment and content being viewed on a device. In addition, a phone can achieve up to 700 nits of peak brightness when "in auto brightness mode under strong light environment," according to the company.

On the other hand, the 700-1,200 nits range is considered a "special mode" that can only be reached through software optimizations.

However, Nothing says this feature is currently unavailable on the phone (1). The company did not say whether it intends to release a software update to fill this gap in the future.

Regardless, the phone (1) is a strong competitor to the best budget Android phones on the market. The handset recently picked up a few improvements, including better third-party charger compatibility, always-on display enhancements, and a few UI tweaks.

Our own Harish Jonnalagadda touted the handset's battery life, clean interface, cameras, wireless charging capability, and unique design with LEDs on the back.

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Nothing sheds light on the phone (1) screen brightness controversy - Android Central

Heliogen, Inc. (NYSE:HLGN) shareholders should be happy to see the share price up 30% in the last month. But that isn't much consolation for the painful drop we've seen in the last year. Indeed, the share price is down a whopping 74% in the last year. It's not uncommon to see a bounce after a drop like that. The real question is whether the company can turn around its fortunes.

Since Heliogen has shed US$65m from its value in the past 7 days, let's see if the longer term decline has been driven by the business' economics.

See our latest analysis for Heliogen

Heliogen wasn't profitable in the last twelve months, it is unlikely we'll see a strong correlation between its share price and its earnings per share (EPS). Arguably revenue is our next best option. When a company doesn't make profits, we'd generally expect to see good revenue growth. That's because fast revenue growth can be easily extrapolated to forecast profits, often of considerable size.

Heliogen grew its revenue by 810% over the last year. That's a strong result which is better than most other loss making companies. So the hefty 74% share price crash makes us think the company has somehow offended market participants. Something weird is definitely impacting the stock price; we'd venture the company has destroyed value somehow. What is clear is that the market is not judging the company on its revenue growth right now. Of course, investors do over-react when they are stressed out, so the sell-off could be unjustifiably severe.

You can see below how earnings and revenue have changed over time (discover the exact values by clicking on the image).

Balance sheet strength is crucial. It might be well worthwhile taking a look at our free report on how its financial position has changed over time.

We doubt Heliogen shareholders are happy with the loss of 74% over twelve months. That falls short of the market, which lost 7.6%. There's no doubt that's a disappointment, but the stock may well have fared better in a stronger market. With the stock down 25% over the last three months, the market doesn't seem to believe that the company has solved all its problems. Given the relatively short history of this stock, we'd remain pretty wary until we see some strong business performance. I find it very interesting to look at share price over the long term as a proxy for business performance. But to truly gain insight, we need to consider other information, too. For instance, we've identified 3 warning signs for Heliogen (2 are potentially serious) that you should be aware of.

If you would prefer to check out another company -- one with potentially superior financials -- then do not miss this free list of companies that have proven they can grow earnings.

Please note, the market returns quoted in this article reflect the market weighted average returns of stocks that currently trade on US exchanges.

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Investors one-year losses grow to 74% as the stock sheds US$65m this past week - Simply Wall St

The Hope and Remembrance memorial was created in 2020 to remember those who have lost their lives to the disease of addiction.

Camden County will host its sixth annual Remembrance and Hope Memorial Vigil in memory of those who lost their lives to a drug overdose on Aug. 31, International Overdose Awareness Day.

Back when we began our opioid addiction task force (in 2014), one of our objectives was to educate the public about the problem of addiction disorders and also to try and remove the stigma that was associated with the disorder, said County Commissioner Louis Cappelli.

We thought by recognizing those who had tragically lost their lives to addiction disorders, we could remove the stigma and educate the public at the same time.

Cappelli noted that factors driving the number of overdoses include abuse of addictive prescription drugs.

When the manufacturers of these pills went to doctors, they said that the pills are not addictive, so our medical community has come to largely depend on opioids for pain treatment, Cappelli explained. Unfortunately, the pills are addictive. The manufacturers knew about it, and therefore we must educate the public on the dangers of taking these prescribed drugs.

A slideshow during the vigil will identify those who lost their lives to overdose and feature speakers who will address the impact of overdose on loved ones. New pavers will also be added to the memorial site.

The CDC estimates that more than 100,000 people in the U.S. have died from a drug overdose between April 2020 and April 2021, an increase of 56,000 compared with the year before. According to nj.gov, New Jersey saw 3,124 suspected drug overdoses in 2021, 335 of them in Camden County.

The vigils keynote speaker will be Mantua resident Tanya Niederman, whose 19-year-old son Justin died earlier this year from fentanyl poisoning.

Im hoping we can find ways to make this (reality) more real so that people can kind of understand, Niederman said, noting that her son was a recreational user of cocaine. I feel like each opportunity I get to tell a story and what happened to him, and our family is an opportunity to save a life.

The reality is that nothing I do or say is going to bring him back, but we can try to keep it from happening to someone else.

Camden County offers many resources on mental health and addiction, including a fentanyl awareness campaign and an addiction task force that recommends policies and initiatives. Cappelli said a policy to make narcan more widely available in schools and buses is in the works.

The Remembrance and Hope Memorial Vigil will take place at 7:30 p.m. at Timber Creek Park in Blackwood. Those interested in having their loved one featured in the slide show should submit their name and picture at https://bit.ly/3dA89L8. People who have submitted pictures in the past will have to resubmit for this year.

Those struggling with addiction can call the county help hotline at (877) 266-8882. To learn more about addiction resources, visit https://www.camdencounty.com/service/mental-health-and-addiction/addiction-resources/

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County vigil to shed light on addictive prescription drugs - The Sun Newspapers

STATEN ISLAND, N.Y. Staten Island is the borough of parks.

Its also the borough of wildlife, particularly squirrels as many borough residents can attest to, the furry animals are in no short supply on our streets and backyards.

So its very possible youve encountered one of the critters lying on the ground with its arms and legs spread out in different directions during the steamy summer weve experienced.

Dont worry, the New York City Parks Department says.

Its actually a healthy activity the creatures are performing called splooting, designed to keep them cool on hot days.

If you see a squirrel lying down like this, dont worry; its just fine, said a Parks Department tweet this week, which included a photo of a sprawled out squirrel. On hot days, squirrels keep cool by splooting (stretching out) on cool surfaces to reduce body heat. It is sometimes referred to as heat dumping.

They arent the only animals that do it, either.

A PennLive.com article from 2020 explained:

Squirrels and chipmunks have always splooted, as have rabbits and foxes and raccoons and many, many other creatures. Birds have their own versions of the sploot. Squirrels also regularly sploot on tree branches.

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Splooting squirrels on Staten Island? NYC Parks sheds light on this bizarre behavior. - SILive.com