Simplified evaluation of CONsciousness disorders (SECONDs) in individuals with severe brain injury: A validation study

BackgroundThe Coma Recovery Scale-Revised (CRS-R) is the gold standard to assess severely brain-injured patients with prolonged disorders of consciousness (DoC). However, the amount of time needed to complete this examination may limit its use in clinical settings. Objective. We aimed to validate a new faster tool to assess consciousness in individuals with DoC.MethodsThis prospective validation study introduces the Simplified Evaluation of CONsciousness Disorders (SECONDs), a tool composed of 8 items: arousal, localization to pain, visual fixation, visual pursuit, oriented behaviors, command-following, and communication (both intentional and functional). A total of 57 individuals with DoC were assessed on 2 consecutive days by 3 blinded examiners: one CRS-R and one SECONDs were performed on 1 day, whereas 2 SECONDs were performed on the other day. A Mann-Whitney U test was used to compare the duration of administration of the SECONDs versus the CRS-R, and weighted Fleiss’ kappa coefficients were used to assess inter-/intra-rater reliability as well as concurrent validity.ResultsIn the 57 participants, the SECONDs was about 2.5 times faster to administer than the CRS-R. The comparison of the CRS-R versus the SECONDs on the same day or the best of the 3 SECONDs led to “substantial” or “almost perfect” agreement (kappa coefficients ranging from 0.78 to 0.85). Intra-/inter-rater reliability also showed almost perfect agreement (kappa coefficients from 0.85 to 0.91 and 0.82 to 0.85, respectively).ConclusionsThe SECONDs appears to be a fast, reliable and easy-to-use scale to diagnose DoC and may be a good alternative to other scales in clinical settings where time constraints preclude a more thorough assessment.     

Haplotype tagging reveals parallel formation of hybrid races in two butterfly species

Genetic variation segregates as linked sets of variants or haplotypes. Haplotypes and linkage are central to genetics and underpin virtually all genetic and selection analysis. Yet, genomic data often omit haplotype information due to constraints in sequencing technologies. Here, we present “haplotagging,” a simple, low-cost linked-read sequencing technique that allows sequencing of hundreds of individuals while retaining linkage information. We apply haplotagging to construct megabase-size haplotypes for over 600 individual butterflies (Heliconius erato and H. melpomene), which form overlapping hybrid zones across an elevational gradient in Ecuador. Haplotagging identifies loci controlling distinctive high- and lowland wing color patterns. Divergent haplotypes are found at the same major loci in both species, while chromosome rearrangements show no parallelism. Remarkably, in both species, the geographic clines for the major wing-pattern loci are displaced by 18 km, leading to the rise of a novel hybrid morph in the center of the hybrid zone. We propose that shared warning signaling (Müllerian mimicry) may couple the cline shifts seen in both species and facilitate the parallel coemergence of a novel hybrid morph in both comimetic species. Our results show the power of efficient haplotyping methods when combined with large-scale sequencing data from natural populations.

Understanding how changes in DNA sequence affect traits and shape the evolution of populations and species has been a defining goal in genetics and evolution (1–3). DNA is naturally organized in the genome as long molecules consisting of linked chromosome segments. Linkage is a core concept in genetics: in genetic mapping, geneticists map causal variants not by tracking the actual mutation but through many otherwise neutral and unremarkable linked variants. Likewise, the detection of selection relies on observing hitchhiking of linked variants rather than seeing the mutation itself. This recognition makes it all the more paradoxical that haplotype information is routinely omitted from most genomic studies as a technical compromise. Lacking haplotype information not only complicates analysis and ancestry reconstruction but also precludes detection of allele-specific expression (4) and chromosome rearrangements and reduces power to detect selective sweeps, even entirely missing them when multiple haplotypes sweep together (5). Instead of sequencing genomes as haplotypes, short-read sequencing produces 150-bp reads. Until long-read platforms become sufficiently accurate and affordable, this lack of haplotype context will continue to impact mapping and genomic studies, particularly those in nonmodel organisms.One way to simplify haplotype reconstruction and inference from sequencing data is to avoid discarding haplotype information in the first place. A promising emerging technique is linked-read (LR) sequencing (6–9), which preserves long-range information via molecular barcoding of long DNA molecules before sequencing. Individual short reads can then be linked via a shared barcode to reconstruct the original haplotype. However, existing options all suffer from high cost, poor scalability, and/or require custom sequencing primers or settings that have thus far prevented them from being applied as the default sequencing platform (SI Appendix, Tables S1 and S2). If LR sequencing could become scalable and affordable, it would significantly advance genetics by enabling the “haplotyping” of entire populations (i.e., the sequencing and systematic discovery of genomic variants as haplotypes in hundreds or even thousands of samples in model and nonmodel organisms alike).Here, we describe a solution called “haplotagging,” a simple and rapid protocol for LR sequencing. Importantly, haplotagging maintains full compatibility with standard Illumina sequencing and can easily scale to large populations with no extra costs. We demonstrate this in three steps. First, we show that direct haplotyping using haplotagging is robust in single human and mouse samples with known haplotypes (“phases”). Next, we show the feasibility of population haplotyping in 245 mice, even with very low-coverage LR sequencing. Finally, we apply haplotagging to investigate the emergence of a hybrid morph in a hybrid zone system in Ecuador featuring 670 individuals of two species of Heliconius butterflies.     

From the Cover: COVID-19 lockdown induces disease-mitigating structural changes in mobility networks

In the wake of the COVID-19 pandemic many countries implemented containment measures to reduce disease transmission. Studies using digital data sources show that the mobility of individuals was effectively reduced in multiple countries. However, it remains unclear whether these reductions caused deeper structural changes in mobility networks and how such changes may affect dynamic processes on the network. Here we use movement data of mobile phone users to show that mobility in Germany has not only been reduced considerably: Lockdown measures caused substantial and long-lasting structural changes in the mobility network. We find that long-distance travel was reduced disproportionately strongly. The trimming of long-range network connectivity leads to a more local, clustered network and a moderation of the “small-world” effect. We demonstrate that these structural changes have a considerable effect on epidemic spreading processes by “flattening” the epidemic curve and delaying the spread to geographically distant regions.

During the first phase of the coronavirus disease 2019 (COVID-19) pandemic, countries around the world implemented a host of containment policies aimed at mitigating the spread of the disease (1–4). Many policies restricted human mobility, intending to reduce close-proximity contacts, the major driver of the disease’s spread (5). In Germany, these policies included border closures, travel bans, and restrictions of public activity (school and business closures), paired with appeals by the government to avoid trips voluntarily whenever possible (6). We refer to these policies as “lockdown” measures for brevity.Based on various digital data sources such as mobile phone data or social media data, several studies show that mobility significantly changed during lockdowns (7). Most studies focused on general mobility trends and confirmed an overall reduction in mobility in various countries (8–12). Other research focused on the relation between mobility and disease transmission: For instance, it has been argued that mobility reduction is likely instrumental in reducing the effective reproduction number in many countries (13–17), in agreement with theoretical models and simulations, which have shown that containment can effectively slow down disease transmission (18–20).However, it remains an open question whether the mobility restrictions promoted deeper structural changes in mobility networks and how these changes impact epidemic spreading mediated by these networks. Recently, Galeazzi et al. (21) found increased geographical fragmentation of the mobility network. A thorough understanding of how structural mobility network changes impact epidemic spreading is needed to correctly assess the consequences of mobility restrictions not only for the current COVID-19 pandemic, but also for similar scenarios in the future.Here, we analyze structural changes in mobility patterns in Germany during the COVID-19 pandemic. We analyze movements recorded from mobile phones of 43.6 million individuals in Germany. Beyond a general reduction in mobility, we find considerable structural changes in the mobility network. Due to the reduction of long-distance travel, the network becomes more local and lattice-like. Most importantly, we find a changed scaling relation between path lengths and geographic distance: During lockdown, the effective distance (and arrival time in spreading processes) to a destination continually grows with geographic distance. This shows a marked reduction of the “small-world” characteristic, where geographic distance is usually of lesser importance in determining path lengths (22, 23). Using simulations of a commuter-based susceptible-infected-removed (SIR) model, we demonstrate that these changes have considerable practical implications as they suppress (or “flatten”) the curve of an epidemic remarkably and delay the disease’s arrival between distant regions.     

Bacterial isolates from infected wounds and their antibiotic susceptibility pattern: some remarks about wound infection

Wound infection plays an important role in the development of chronicity, delaying wound healing. This study aimed to identify the bacterial pathogens present in infected wounds and characterise their resistance profile to the most common antibiotics used in therapy. Three hundred and twelve wound swab samples were collected from 213 patients and analysed for the identification of microorganisms and for the determination of their antibiotic susceptibility. Patients with diverse type of wounds were included in this retrospective study, carried out from March to September 2012. A total of 28 species were isolated from 217 infected wounds. The most common bacterial species detected was Staphylococcus aureus (37%), followed by Pseudomonas aeruginosa (17%), Proteus mirabilis (10%), Escherichia coli (6%) and Corynebacterium spp. (5%). Polymicrobial infection was found in 59 (27·1%) of the samples and was mainly constituted with two species. The most common association was S. aureus/P. aeruginosa. All Gram‐positives were susceptible to vancomycin and linezolid. Gram‐negatives showed quite high resistance to the majority of antibiotics, being amikacin the most active against these bacteria. This study is mostly oriented to health care practitioners who deal with wound management, making them aware about the importance of wound infection and helping them to choose the adequate treatment options to control microbial infection in wounds.