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In the article by Miller and colleagues ([1]), the authors identified an error in Appendix Table 2. The values for rifles owned and shotguns owned were reversed in the "Average number of firearms owned" section. The headings for these rows have been corrected so the values align with the appropriate type of firearm. [Extracted from the article]

Purpose: Our objective was to investigate rural adolescents' use of firearms and whether they had received firearm training. Methods: 2019 Iowa FFA Leadership Conference attendees were surveyed. Descriptive and comparative analyses were performed. Results: One thousand three hundred and eighty‐two FFA members aged 13‐18 years participated. The vast majority (85%) had fired a rifle/shotgun; 58% reported firing them >20 times. Of those who had fired rifles/shotguns, 32% had done so before 9 years old; 79% before 13 years. Most had also fired a handgun (62%), with 30% having fired handguns >20 times. Of those who had fired handguns, 34% had done so before 11 years old. The average age for first firing rifles/shotguns was 10.1 (SD 2.9) years, and 11.9 (SD 2.8) years for handguns. Males, older teenagers, and those living on farms or in the country had significantly greater percentages that had fired a rifle/shotgun or a handgun. Greater proportions of males had used firearms >20 times and started firing them at younger ages. Over half (55%) reported having gone hunting. Of those, 24% first hunted before 9 years old; 48% before 11 years. Of those who had used a firearm, 61% had completed a firearm safety training course. For hunters, 80% had taken a course. Conclusions: Most participants had used firearms, and many did so at very young ages. Substantial numbers had not received formal training. The authors believe that families should be counseled when it is developmentally appropriate to introduce youth to firearms, and all should take firearm safety training before using them. [ABSTRACT FROM AUTHOR]

Osteomyelitis of the jaw is a severe inflammatory disorder that affects bones, and it is categorized into two main types: chronic bacterial and nonbacterial osteomyelitis. Although previous studies have investigated the association between these diseases and the oral microbiome, the specific taxa associated with each disease remain unknown. In this study, we conducted shotgun metagenome sequencing (≥10 Gb from ≥66,395,670 reads per sample) of bulk DNA extracted from saliva obtained from patients with chronic bacterial osteomyelitis (N = 5) and chronic nonbacterial osteomyelitis (N = 10). We then compared the taxonomic composition of the metagenome in terms of both taxonomic and sequence abundances with that of healthy controls (N = 5). Taxonomic profiling revealed a statistically significant increase in both the taxonomic and sequence abundance of Mogibacterium in cases of chronic bacterial osteomyelitis; however, such enrichment was not observed in chronic nonbacterial osteomyelitis. We also compared a previously reported core saliva microbiome (59 genera) with our data and found that out of the 74 genera detected in this study, 47 (including Mogibacterium) were not included in the previous meta-analysis. Additionally, we analyzed a core-genome tree of Mogibacterium from chronic bacterial osteomyelitis and healthy control samples along with a reference complete genome and found that Mogibacterium from both groups was indistinguishable at the core-genome and pan-genome levels. Although limited by the small sample size, our study provides novel evidence of a significant increase in Mogibacterium abundance in the chronic bacterial osteomyelitis group. Moreover, our study presents a comparative analysis of the taxonomic and sequence abundances of all genera detected using deep salivary shotgun metagenome data. The distinct enrichment of Mogibacterium suggests its potential as a marker to distinguish between patients with chronic nonbacterial osteomyelitis and chronic bacterial osteomyelitis, particularly at the early stages when differences are unclear. [ABSTRACT FROM AUTHOR]

Polyethylene terephthalate (PET) is the conventional plastic used for packaging but disposal of PET in the natural environment poses significant threats to the ecological system. In the biological process, the degradation of PET plastic using PET-degrading enzyme (PETase) has attracted scientific interest, thus increasing the exploration of PETase evolution. This study aims to exploit a new PETase from a greenhouse farm in Tainan, Taiwan. The in-silico approach of shotgun metagenomic analysis, enzyme structural prediction, molecular docking and simulations were applied, thus narrowing the cleft and identifying a potential PETase-like enzyme, denoted as F148. The heterologous expression of F148 in Escherichia coli strains was capable of breaking down PET into terephthalate monomer (TPA). Furthermore, the PET-degrading ability of F148 was optimized using the incorporation of MHETase and Colicin E7 via in vitro and in vivo digestion methods, respectively. The in vivo system served as a concise approach due to allowing simultaneous F148 secretion, thus achieving the highest TPA amount of 0.56 mg, equivalent to 11.2% weight loss of PET. This study not only contributes promising insights into the exploration of a distinctive gene but also successfully invents an efficient PETase using a one-step in vivo process in genetically modified E. coli. [Display omitted] • A novel F148 PETase was discovered from untapped source of local soil metagenomics. • Escherichia coli Lemo21 was consigned as an efficient host for expression of F148. • In vitro reaction of MHETase and F148 synergistically promotes PET degradation. • Co-expressing colicin E7 lysis and F148 invents a one-step PET degradation method. • The highest degradation efficiency is 0.56 mg-TPA/g-protein via in vivo process at 120 h. [ABSTRACT FROM AUTHOR]

Shotgun sequencing is a high-throughput method used to detect copy number variants (CNVs). Although there are numerous CNV detection tools based on shotgun sequencing, their quality varies significantly, leading to performance discrepancies. Therefore, we conducted a comprehensive analysis of next-generation sequencing-based CNV detection tools over the past decade. Our findings revealed that the majority of mainstream tools employ similar detection rationale: calculates the so-called read depth signal from aligned sequencing reads and then segments the signal by utilizing either circular binary segmentation (CBS) or hidden Markov model (HMM). Hence, we compared the performance of those two core segmentation algorithms in CNV detection, considering varying sequencing depths, segment lengths and complex types of CNVs. To ensure a fair comparison, we designed a parametrical model using mainstream statistical distributions, which allows for pre-excluding bias correction such as guanine-cytosine (GC) content during the preprocessing step. The results indicate the following key points: (1) Under ideal conditions, CBS demonstrates high precision, while HMM exhibits a high recall rate. (2) For practical conditions, HMM is advantageous at lower sequencing depths, while CBS is more competitive in detecting small variant segments compared to HMM. (3) In case involving complex CNVs resembling real sequencing, HMM demonstrates more robustness compared with CBS. (4) When facing large-scale sequencing data, HMM costs less time compared with the CBS, while their memory usage is approximately equal. This can provide an important guidance and reference for researchers to develop new tools for CNV detection. [ABSTRACT FROM AUTHOR]

When reconstructing a shooting incident with a shotgun, the muzzle‐to‐target distance can be determined by relating the size of a dispersion pattern found on a crime scene to that of test shots. Ideally, the test shots are performed with the weapon and ammunition that were used in the incident. But sometimes examiners will have to resort to alternatives, such as using cartridges of the same brand and type but with another pellet size. For this reason, the relationship between pellet size and shotgun dispersion patterns was studied with both lead and steel shotgun pellets. Cartridges were loaded with identical cartridge cases, powder charges, and wads but with different pellet sizes, below size B. The cartridges were fired, and the dispersion patterns at 5 m in front of the muzzle were measured and compared. The results provide strong support for the proposition that shotgun dispersion patterns with both lead and steel shot increase with decreasing pellet size if all other relevant parameters are kept equal. The results also provide an indicative measure of the magnitude of the effect. Pattern sizes were approximately 1.7 times larger with #9 than with #0 lead shot and 1.4 times larger with #9 than with #1 steel shot. The differences between consecutive shot sizes were generally smaller. This means that cartridges of equal brand and type but with the next nearest shot number can be used for a muzzle‐to‐target distance determination, keeping the information of the current study in mind in the final interpretation of the results. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC), the third most common cancer globally, has shown links to disturbed gut microbiota. While significant efforts have been made to establish a microbial signature indicative of CRC using shotgun metagenomic sequencing, the challenge lies in validating this signature with 16S ribosomal RNA (16S) gene sequencing. The primary obstacle is reconciling the differing outputs of these two methodologies, which often lead to divergent statistical models and conclusions. In this study, we introduce an algorithm designed to bridge this gap by mapping shotgun-derived taxa to their 16S counterparts. This mapping enables us to assess the predictive performance of a shotgun-based microbiome signature using 16S data. Our results demonstrate a reduction in performance when applying the 16S-mapped taxa in the shotgun prediction model, though it retains statistical significance. This suggests that while an exact match between shotgun and 16S data may not yet be feasible, our approach provides a viable method for comparative analysis and validation in the context of CRC-associated microbiome research. [ABSTRACT FROM AUTHOR]

Viruses are the most abundant 'biological entities' in the world's oceans. However, technical and methodological constraints limit our understanding of their diversity, particularly in benthic abyssal ecosystems (>4000 m depth). To verify advantages and limitations of analyzing virome DNA subjected either to random amplification or unamplified, we applied shotgun sequencing-by-synthesis to two sample pairs obtained from benthic abyssal sites located in the North-eastern Atlantic Ocean at ca. 4700 m depth. One amplified DNA sample was also subjected to single-molecule long-read sequencing for comparative purposes. Overall, we identified 24,828 viral Operational Taxonomic Units (vOTUs), belonging to 22 viral families. Viral reads were more abundant in the amplified DNA samples (38.5–49.9%) compared to the unamplified ones (4.4–5.8%), with the latter showing a greater viral diversity and 11–16% of dsDNA viruses almost undetectable in the amplified samples. From a procedural point of view, the viromes obtained by direct sequencing (without amplification step) provided a broader overview of both ss and dsDNA viral diversity. Nevertheless, our results suggest that the contextual use of random amplification of the same sample and long-read technology can improve the assessment of viral assemblages by reducing off-target reads. [ABSTRACT FROM AUTHOR]

The occurrence and spread of antibiotic resistance genes (ARGs) in environmental microorganisms, particularly in poly-extremophilic bacteria, remain underexplored and have received limited attention. This study aims to investigate the prevalence of ARGs and metal resistance genes (MRGs) in shotgun metagenome sequences obtained from water and salt crust samples collected from Lake Afdera and the Assale salt plain in the Danakil Depression, northern Ethiopia. Potential ARGs were characterized by the comprehensive antibiotic research database (CARD), while MRGs were identified by using BacMetScan V.1.0. A total of 81 ARGs and 39 MRGs were identified at the sampling sites. We found a copA resistance gene for copper and the β-lactam encoding resistance genes were the most abundant the MRG and ARG in the study area. The abundance of MRGs is positively correlated with mercury (Hg) concentration, highlighting the importance of Hg in the selection of MRGs. Significant correlations also exist between heavy metals, Zn and Cd, and ARGs, which suggests that MRGs and ARGs can be co-selected in the environment contaminated by heavy metals. A network analysis revealed that MRGs formed a complex network with ARGs, primarily associated with β-lactams, aminoglycosides, and tetracyclines. This suggests potential co-selection mechanisms, posing concerns for both public health and ecological balance. [ABSTRACT FROM AUTHOR]