Here, we aim to confidently identify minor-effect loci contributing to the highly polygenic basis of the long-term, bi-directional selection responses for 56-d body weight in the Virginia body weight chicken lines. To achieve this, a strategy was developed to make use of data from all generations (2F–18F) of the advanced intercross line, developed by crossing the low and high selected lines after 40 generations of selection. A cost-efficient low-coverage sequencing based approach was used to obtain high-confidence genotypes in 1Mb bins across 99.3% of the chicken genome for >3,300 intercross individuals. In total, 12 genome-wide significant, and 30 additional suggestive QTL reaching a 10% FDR threshold, were mapped for 56-d body weight. Only 2 of these QTL reached genome-wide significance in earlier analyses of the 2F generation. The minor-effect QTL mapped here were generally due to an overall increase in power by integrating data across generations, with contributions from increased genome-coverage and improved marker information content. The 12 significant QTL explain >37% of the difference between the parental lines, three times more than 2 previously reported significant QTL. The 42 significant and suggestive QTL together explain >80%. Making integrated use of all available samples from multiple generations in experimental crosses are economically feasible using the low-cost, sequencing-based genotyping strategies outlined here. Our empirical results illustrate the value of this strategy for mapping novel minor-effect loci contributing to complex traits to provide a more confident, comprehensive view of the individual loci that form the genetic basis of the highly polygenic, long-term selection responses for 56-d body weight in the Virginia body weight chicken lines.Genetic dissection of highly polygenic traits is a challenge, in part due to the power necessary to confidently identify loci with minor effects. Experimental crosses are valuable resources for mapping such traits. Traditionally, genome-wide analyses of experimental crosses have targeted major loci using data from a single generation (often the  Drosophila melanogaster  studies in experimental crosses have high power, resolution is limited due to the extensive linkage disequilibrium (LD) introduced by the crossing design. This meant that, historically, it was possible to use the sparse or very sparse marker maps available at the time without compromising the resolution much further, as resolution was primarily gated by the number of recombinations. Still, this resulted in regions of the genome with less coverage and, in some cases, missing data on small chromosomes and/or chromosomal ends  and high (HWS) weight lines after 40 generations of selection . Efforts include genome-wide mapping  were whole-genome re-sequenced to ∼30X coverage  were sequenced to ∼0.4X coverage using the strategy outlined by BWA . For this, high-coverage sequence data from the outbred founders of the population and low-coverage sequence data from the intercross individuals, generated as described below, were used.Deep-Stripes is an extension of Stripes  reverting to hardcoded genotype emission thresholds in cases where the original nonlinear minimization procedure for determining these (ng these  failed dDeep-Stripes implemented genotype estimation in both directions on the chromosome. Genotype estimation in this way with 2 window-sizes (50 and 200 markers) reduces the number of false positive crossovers in marker-dense areas resulting from the increased flat, per-window genotype estimation error rate in high marker density regions. The final genotypes were assigned by transforming the output from each of the four runs (2 directions × 2 window-sizes) described above to a genotype matrix. These contained estimated founder line genotypes for each individual in even-sized (1 Mb) bins across the genome. The process for this used the 4 matrices as follows: In bins where no recombination event was inferred, the genotypes were coded as numerical values  corresponding to the homozygote for founder-line 1, heterozygote and homozygote for founder-line 2, respectively. Recombination breakpoints were estimated with bp resolution. However, if one or more recombination events were detected, resulting in multiple genotypes being called in a 1 Mb window, the genotype was scored on a continuous scale from 1 to −1 by averaging the founder genotypes scores across the base pairs in the segment. Second, the genotype matrices from the forward and reverse runs were filtered by considering bins where estimates differed by more than one recombination event as uninformative and setting them to missing. This procedure was performed separately for the two window-sizes (50 and 200 markers). Third, the forward- and reverse-filtered genotype matrices obtained using 50 and 200 marker window-sizes were combined. This was done by using the genotype derived using 50 marker sliding windows if the marker density was below 20 markers/Mb and 200 if above. Bins with ambiguous genotypes were set to missing. These were defined as bins with genotype scores in the ranges  and . Finally, all bins genotyped in less than 100 individuals were set to missing.In order to detect incoherently called genotypes in low-information areas due to a delay of inferred crossovers to the end of such regions, LOD) score of 4.01.QTL mapping was performed for body weight at 8 wk of age. To correct for generational effects, 8-wk body weights were standardized within each generation. This was done by subtracting the generation mean from each observation and then dividing it by the within-generation standard deviation. The genome scan was performed using the ‘scanone’ function from the package ‘qtl’  in R R  using HaFDR) threshold. First, LOD scores for markers across the genome were transformed into P-values . Due to the standardization of the phenotypes, the estimates were multiplied with the population-standard deviation to obtain an estimate in grams. The sum of these estimates was then expressed as a fraction of the between-line difference. The same was also done by fitting all QTL jointly (Table S4).To estimate the residual variance explained by the QTL, we corrected for the sex effect using a linear model and fit either 1) all 12 significant or 2) all 42 suggestive and significant QTL using the IC) was calculated across the genome in the set of 2F individuals that were common to this study and the earlier one of the 2F generation (The information content (neration . The meaneration :(2)IC=VAa and d indicator regression variables from This was calculated at individual marker locations, as well as every cM, across the genome using the The information content was compared at the physical locations Mb;  of the gF2-F18 = 3,327 AIL individuals that passed quality control. The average density of informative markers across the genome was 102 markers/Mb. Because the founder lines are outbred and the individual AIL offspring descend from different founders, the number of informative SNPs varied among individuals and decreased over generations, as more ancestors contributed to the genotype of each individual. Founder-line genotypes were obtained for all of the 1,058 1Mb bins defined on the 33 largest chromosomes, identifying an average of 74 recombination breakpoints per Mb across all individuals before filtering. The accuracy of this imputation approach was previously validated on a subset of the individuals against experimentally determined SNP genotypes by 2F population by After sequencing and variant calling, genotypes were estimated for nSince the founder lines were outbred, only a small fraction of the total markers genotyped for each individual are informative for line origin, averaging between 1.4 and 8% . While these are more than sufficient to impute founder-line haplotypes with confidence, the trade-off for this type of imputation is that it does not account for variation within the outbred lines, which limits detection power for QTL that do not differ between the lines or segregate in only one line. While this trade-off for confidence and power in detecting QTLs is a limitation, it is due to the imputation, and could be supplemented by alternative approaches on the same sequencing data for subsequent finemapping.As illustrated in line AIL  were alsline AIL ; Table 1Several fine-mapping studies  have beeneration  and 2) 9neration . Here, 2ome-scan  reached ome-scan , Table 1Growth6, reaching genome-wide significance for 56-d body weight in the latest genome scan of the 2F generation . deep-Stripes inferred genotypes  to the genotypes from the SNP and micro-satellite based marker panel used in Wahlberg et al.  as an illustration of how the additional resolution facilitated separation of the 4/23 and 4/36 loci.In addition, the approach used here provided sufficient resolution to partition F8 to F18 populations (The AIL was produced by intercrossing chickens from HWS and LWS after 40 generations of selection (S41), and the founders for the intercross differed more than 8-fold  in 56-d body weight . Estimatal lines . This coulations . The 42 Growth1 on Chromosome 1 between Growth6 on Chromosome 4 (20.7 ± 4.4 g) was lower than the previous estimate (36.3 ± 8.3 g). However, the current approach found 3 significant and 2 suggestive QTL on Chromosome 4. Of these, 2 significant and one suggestive QTL were within a QTL region (Growth7) reported in the first analysis of the 2F generation (Growth9 on Chromosome 7 had a reduced effect size estimate (25.6 ± 4.0 g), compared to the 43.2 ± 9 g estimated by The difference in effect size estimates for neration , but latA genome-wide significant QTL was mapped to 27 Mb on Chromosome 1 . This reThe low coverage sequencing approach implemented here improved the genome-wide marker coverage in this population. In particular, the coverage was improved by reaching further out to the ends of almost all chromosomes, specifically 5, 6, and 8 . As a reIn this study, the intercross population was bred from the Virginia body weight selection lines. These two pedigreed populations were divergently selected long-term for a single trait, 56-d body weight, for 40 generations before the intercross was formed. Previous whole-genome analyses of this intercross were challenged to detect genome-wide significant QTL, finding only 1  or 2 Wa. SubsequGrowth1. The reduced effect size for Growth9 was not surprising, given that Chromosome 7, and Growth9 in particular, were involved in epistatic interactions , which is >3 times the founder-line difference explained by the significant QTL in Further, 30.3% of the bins on the covered autosomal chromosomes were statistically associated with the phenotype after lenient adjustment for multiple testing. This means that any overlap between the extended QTL and the selective sweep regions should be interpreted with caution. Still, this supports the interpretation of a high polygenicity of the investigated trait, and it is reasonable to believe that these regions were undergoing true and detectable selection, given the strong single trait artificial selection regime in the selected lines .Genotyping by sequencing has provided opportunities to both increase marker density and decrease the cost for genotyping. Strategies based on low-coverage sequencing approaches open cost-efficient opportunities to re-analyze existing experimental populations. Here, we demonstrated one such approach targeted to deep outbred intercrosses. This approach increased the overall marker coverage of the chicken genome from 93% , as estiBeyond Markers that are fixed and therefore always informative for line-origin, outbred founder lines have many more markers that can be line-informative since they are fixed between the HWS and LWS founders contributing to the ancestry of an individual, despite still segregating within the lines. However, as the number of these markers decreases with the increase in unique ancestors contributing to each individual in later generations, the depth of generations one can investigate with this can be limited by the number of founding individuals as well as the size of each generation. Thus, it is important that the founder lines are sufficiently divergent to provide a minimum resolution via fixed markers. However, because the nonphysical window size of the imputation process does not sacrifice higher resolution in divergent regions, it is likely that any cross between lines with a significant genetic component to their phenotypic divergence will have sufficient coverage with informative markers in regions of interest. For the AIL used in this study, it is likely that the resolution is not limited by the marker coverage, but rather by both the 1 Mb binning approach and the 50/200 marker sliding window imputation of founder genotypes. This is because there were enough accumulated recombinations in the later generations that were lost through these approaches. While these were chosen for robustness and provided an appropriate resolution/power trade-off across all individuals, modifying these parameters could provide added opportunities for fine mapping.2F population, but without a definitive peak. This is likely due to a lack of recombination events distal to the peak in the 2F population, though the LOD scores for the outermost 3 Mb were above the 5% permutation threshold for chromosome-wide significance.The increased marker density also increased the information content throughout the genome, which further increased power in the QTL analyses. In particular, coverage was extended at the ends of the larger chromosomes. This led to locating a novel suggestive QTL on Chromosome 8 that was primarily due to increase in coverage, though the increase in power helped elevate and define the peak of the QTL. It was seen in the full marker set using only the Stripes pipeline. Additional work beyond this study is needed to estimate the genotype in these regions using alternative genotyping or bioinformatics approaches before they can be included in QTL mapping studies.In addition, two additional small linkage groups were also covered. The minor regions of the genome that remain are present in scaffolds containing too few markers for reliable genotype estimation using the Integrating data across an AIL population provides value to the QTL scan by helping to map new QTL and refining the resolution and explanatory power of existing ones. This is particularly so when intermediate generations already exist due to previous attempts in fine mapping. At less than 1 EUR/sample for library preparation and 10 EUR/sample for library preparation and sequencing , our appFor AIL populations with smaller intermediate generations and multiple siblings or half sibs, correcting generation with a mixed or fixed effect model will likely result in overcorrection due to the correlation between generations and kinship. Standardization using z-scores provides an acceptable trade-off between overcorrecting and confidence in accounting for generational batch effects.In conclusion, this study represents a more comprehensive study of the individual loci forming the genetic basis of the highly polygenic, long-term selection responses on 56-d body weight in the Virginia chicken lines, to date. It contributes not only to our current understanding of the genetic basis of body weight in chickens, but also provides a solid methodological foundation to further investigate the genetic architecture of complex traits in populations with similar designs.


Infection with Influenza A virus (IAV) causes the well-known symptoms of the flu, including fever, loss of appetite, and excessive sleepiness. These responses, mediated by the brain, will normally disappear once the virus is cleared from the system, but a severe respiratory virus infection may cause long-lasting neurological disturbances. These include encephalitis lethargica and narcolepsy. The mechanisms behind such long lasting changes are unknown. The hypothalamus is a central regulator of the homeostatic response during a viral challenge. To gain insight into the neuronal and non-neuronal molecular changes during an IAV infection, we intranasally infected mice with an H1N1 virus and extracted the brain at different time points. Using single-nucleus RNA sequencing (snRNA-seq) of the hypothalamus, we identify transcriptional effects in all identified cell populations. The snRNA-seq data showed the most pronounced transcriptional response at 3 days past infection, with a strong downregulation of genes across all cell types. General immune processes were mainly impacted in microglia, the brain resident immune cells, where we found increased numbers of cells expressing pro-inflammatory gene networks. In addition, we found that most neuronal cell populations downregulated genes contributing to the energy homeostasis in mitochondria and protein translation in the cytosol, indicating potential reduced cellular and neuronal activity. This might be a preventive mechanism in neuronal cells to avoid intracellular viral replication and attack by phagocytosing cells. The change of microglia gene activity suggest that this is complemented by a shift in microglia activity to provide increased surveillance of their surroundings. When you are ill, your behaviour changes. You sleep more, eat less and are less likely to go out and be active. This behavioural change is called the ‘sickness response’ and is believed to help the immune system fight infection.An area of the brain called the hypothalamus helps to regulate sleep and appetite. Previous research has shown that when humans are ill, the immune system sends signals to the hypothalamus, likely initiating the sickness response. However, it was not clear which brain cells in the hypothalamus are involved in the response and how long after infection the brain returns to its normal state.To better understand the sickness response, Lemcke et al. infected mice with influenza then extracted and analysed brain tissue at different timepoints. The experiments showed that the major changes to gene expression in the hypothalamus early during an influenza infection are not happening in neurons – the cells in the brain that transmit electrical signals and usually control behaviour. Instead, it is cells called glia – which provide support and immune protection to the neurons – that change during infection. The findings suggest that these cells prepare to protect the neurons from influenza should the virus enter the brain.Lemcke et al. also found that the brain takes a long time to go back to normal after an influenza infection. In infected mice, molecular changes in brain cells could be detected even after the influenza infection had been cleared from the respiratory system.In the future, these findings may help to explain why some people take longer than others to fully recover from viral infections such as influenza and aid development of medications that speed up recovery. In humans as well as other species, Influenza A virus (IAV) infection causes respiratory tract disease, evoking seasonal epidemics associated with increased morbidity and mortality rates . Beyond Some IAV strains are considered neurotropic, meaning that they hold the machinery to infiltrate the CNS and replicate, but evidence suggest that even non-neurotropic influenza strains can infiltrate the brain and cause neurological disturbances . When noThe hypothalamus is a functional integrator of peripheral signals and regulates several physiological and behavioural functions via several specialised nuclei . The difIn the current study, we used intranasal infections with H1N1 pdm09 in female mice and snRNA-seq to follow changes in the transcriptional landscape of cell populations in the medial hypothalamus. We find changes in all populations during the peak of disease. A change in cell state is seen in microglia at early infection and later strong effects are detected in astrocytes and oligodendrocytes. Interestingly, after the mice have recovered from the acute infection, all non-neuronal cell types return to their resting state, while in neurons we detect long lasting changes of the transcriptome even after full recovery.Mice were infected intranasally with influenza virus A/California/07/2009 (H1N1 pdm09) and monitored for a period of 23 days post infection for weight changes. Groups of mice were euthanized at 3, 7, and 23 days post infection (dpi) to collect lung, olfactory bulb (OB) and brain tissue for detection of viral abundance and snRNA-seq. The rationale for choosing these time points of sampling were as follows: (i) 3 dpi as a time point of initial disease onset before the start of weight loss – at this time point innate immunity is peaking; (ii) 7 dpi marks the point of strong weight loss and is where the adaptive immune response takes over, and before potential lethal outcome for some of the animals and (iii) 23 dpi as a recovery group where animals had completely regained their initial body weight .Infected mice started losing weight around 4 dpi and had the highest weight loss at 10 dpi . AnimalsSnap25, Syp, Syt1, Elavl2; For snRNA-seq we dissected a hypothalamic region containing both the DMH and the LHA from brains obtained from infected mice and the control group  or Olig1, Anln, Plp1 in MOLs. Further, we resolved a cluster of astrocytes (NonN_2), which was defined by the differential expression of Slc4a4 and which was also positive for Sox9, Gja1 and Agt. The nearby cluster NonN_7 expressed similar characteristics as the astrocytes and exhibited a high expression of Slit2, Col23a1, and Slc16a2 which have been described to be enriched in tanycytes  and NonN_9  were identified as vascular and leptomeningeal cells, respectively. The NonN_8 cluster expressing Slco1a4 is expressing additionally Ebf1, Flt1, Rgs5, which are described as pericyte marker genes  is expressing general neuron markers, we could not identify any discriminatory markers for inhibitory or excitatory neuron populations ; Vesicular GABA Transporter , glutamate decarboxylase 67 ; and histidine decarboxylase (encoded by Hdc) and separated the neuronal cells in GABAergic and glutamatergic cells (Hdc+) and dopaminergic neurons . The histaminergic neurons were left for visualisation and analysis purposes in this cell type, despite them often being described as GABAergic (Hcrt+) and MCH expressing neurons . The cellular resolution aligns well with the composition of other cell types seen in other studies of the hypothalamus  analysis on the three overall cell populations . Similarly, to the previous pseud-bulk approach, we detected the strong down-regulation of genes at 3 dpi in all the three distinct cell populations . The oveWe tested all identified NonN and neuronal cell populations for differential gene expression. A cell cluster was excluded from analysis if their cells were originating from less than three of the biological replicates and if they were composed of less than two cells per cluster per biological replicate. Similarly, we used for further downstream analysis only strongly expressed genes with high up- or down-regulation . As shown before, the strongest regulatory effect on the different clusters’ transcriptomes were detected at 3 dpi, which lasted partially until 23 dpi . SeveralAs described in the previous paragraph, we identified immune response pathways as one of the most enriched gene ontologies (GO) terms in the dataset. Therefore, we selected only the genes annotated in these immune-related GO terms, leaving us with 22 genes. Interestingly, all these 22 immune related genes are regulated in microglia (NonN_6) at 3 and 7 dpi . In otheWe next aimed to describe the transcriptional changes in the microglia during disease progression. Raw DEG numbers emphasize the microglia cluster as the only cell cluster with a strong upregulation of genes at 3 dpi (75 genes), and a long-lasting down-regulation of genes until 23 dpi  or anti-inflammatory  states of microglia (Meg3), Imprinted in Prader-Willi Syndrome (Ipw) and the gene Fibroblast Growth Factor 14 (Fgf14) which have been recently connected to immune activation and pro-inflammatory microglia states , were expressed in less microglia cells during acute infection than in control and 23 dpi. The results show that microglia are also in the medial hypothalamus a main immune orchestrating cell population, shifting from their homeostatic state towards a pro-inflammatory microglia population during 3 and 7 dpi. This shift is completely resolved after recovery at 23 dpi.Typical markers like cell surface proteins or intracellular markers are often used to identify pro-  and Zfhx3. Zfhx3 showed a very distinct expression in this cluster (In oligodendrocytes (NonN_1) and astrocytes (NonN_2) between 144–244 genes were up- or down-regulated (logFC >0 or logFC <0) at 3 and/or 7 dpi . This wa cluster . For ast cluster . Accordi cluster . TogetheEven though the most prominent feature of the hypothalamic transcriptional response to H1N1 pdm09 was the downregulation of genes at 3 dpi across all cell clusters, some neuronal cell populations showed long-term differential expression of genes. As described in previous paragraphs, we detected overall changes in both GABAergic and glutamatergic neurons at 23 dpi . When lo+/Npy+, Lepr+, and POMC+ neurons; and sleep: Hdc+, Pmch+, and Hcrt+ neurons. With both feeding and sleep patterns changing during influenza, we would expect these neuronal subtypes to change their gene expression during the experiment. This is indeed what we find. While most of the subtypes respond with overall downregulation of gene expression  at 3 dpi with no differential regulation of these transcripts at 7 dpi. This indicate that we do not have neuroinflammatory processes in the hypothalamus in our model, or that the here chosen time point of 3 dpi might be too late to capture innate immune activation in the CNS.In contrast to our results, studies using the PR8 disease model reported elevated mRNA expression levels of cytokines related to viral infections 24 hr and 6 days post infection . We founEven though we used different published datasets to annotate the here-in identified cell types or compare gene expression changes in a disease context, the comparability of these datasets is challenging due to differences in the experimental setups. We identified a lack of RNA sequencing studies focusing solely on molecular changes in the hypothalamus during non-neurotropic IAV challenges. Different molecular changes based on viral strain or between brain regions are expected and further studies are needed to resolve this.A prominent feature of our dataset is a long-lasting down-regulation of gene transcripts until 23 dpi in many neuronal cell populations. Gene set enrichment analysis showed these to be mainly related to processes in mitochondria (i.e. citric acid cycle (TCA), ATP synthesis) or to protein translation in the cytosol. This could be interpreted as a defensive mechanism, for the neurons to escape viral entry or replication in the cell and hence protect the cells from apoptosis. Different intermediates of the citric acid (TCA) cycle can be important for viral replication within the cell. In the context of IAV, acetyl-CoA and ATP are known to be beneficial for viral replication. Acetyl-CoA is also used for viral acetylation and ATP is needed for budding of the virus . The dow+/Npy+, Lepr+, POMC+, Hdc+, Pmch+, and Hcrt+ neurons showed a particularly strong response of the POMC+ neurons compared to the other neuronal subtypes. POMC+ neurons have been shown before to be activated during inflammation in various models, a mechanism that is speculated to be important for development of sickness behaviours  were found down-regulated at 3 and partly at 7 dpi, indicating a clear shift in microglia homeostasis at 3 dpi. This assumption is supported by the activation of genes contributing to cell adhesion, signalling and differentiation as well as synapse signalling. A pro-inflammatory microglia state (M1) is induced during an acute immune response and provide acute defence and repair mechanisms. For this, a tight control via constant cell-to-cell communication is necessary to avoid excessive long-term activation and eventual neurodegenerative consequences  in the 7-dpi population. This protein belongs to the family of glucocorticoid receptors (GCR) and has been associated with several environmental pertubations, including its up-regulation in the CNS during stress or during fasting in obese individuals  transcript. Lrrc8a has putative regulatory transmembrane transporter functions and is one essential subunit of volume-regulated anion channels (VRAC). Hence, it plays an important role in cell volume changes and regulate synaptic transmission via glutamate release from astrocytes (zinc finger homeobox 3 (Zfhx3) transcript is specifically up-regulated in astrocytes at 7 dpi. Its genomic locus has been associated with body mass index and it is further known to influence circadian rhythm and sleep , which is contributing to retinol metabolism. The inhibition of Aldh1a1 leads to reduced weight gain  transporters   , which w Slc1a3) . Additio Slc1a3) , so what Slc1a3) . This co Slc1a3) . At the  Slc1a3) . This miIn summary, the data presented here shows how the cellular state of non-neuronal and neuronal cell populations in the hypothalamus change during peripheral IAV. Our data suggests the existence of tight co-regulation where one cell type affects the state of another . This coThe experiments conducted in the study were approved by the Danish Animal Experimental Inspectorate (license number: 2020-15-0201-00585) in accordance with the Directive 2010/63/EU of the European Parliament and Council on the protection of animal used for scientific purposes. Female C57BL/6NTac mice were obtained from Taconic at 8–10 weeks old. Animals were randomly split into groups of 5, fed ad libitum and kept at a 12 h day/night cycle. Virus stocks  were maintained at Prof. Jan P. Christensen Lab, University of Copenhagen. Virus stocks were propagated in Madin-Barby Canine Kidney cells (MDCK). Cell cultures were infected at 0,01 MOI and harvested at 90–100% CPE after 2–3 days. Supernatant was centrifuged to clear it of cell debris and supernatant was stored at –80 °C for titration and infection of animals. Titration was done as previously described by Uddbäck and colleagues .5 pfu in 30 µl per animal. Three days, 7 days, and 23 days post inoculation (dpi) a group of five animals was randomly selected and sacrificed by cervical dislocation between 1:00-3:00 pm to minimize confounding effects from circadian rhythm or sleep/wake phases. The lungs, brains, and olfactory bulbs were removed, frozen on dry ice and stored in at –80°C. The lungs and olfactory bulb were used to determine infection and the hypothalamus of the brain was used for snRNAseq.Ten-week-old female C57BL/6 N were inoculated intranasally with H1N1 A/California/07/2009 with a dose of 5x10The olfactory bulbs and the right lung were homogenized using an ULTRA-Turrax T8.01 homogenizer in the RLT lysis buffer from the RNeasy mini and midi kit (Qiagen). The rest of the RNA extraction was performed according to the manufactured protocol from the RNeasy mini and midi kit to extract RNA from brain and lung tissue. The concentration and purity of RNA in the samples was measured by a Nanodrop spectrophotometer . All the samples used in this experiment had a 1.7–2.1 260/280 nm ratio. Next, the samples were diluted to 1 µg total RNA in each well and cDNA was synthesized by reverse transcription using the QuantiTect Reverse Transcription Kit.Mouse brains tissue were sectioned at 300 μm in a cryostat [Leica CM3050S] to identify the lateral hypothalamus (bregma –0.94 to –1.34) for punching. The sections were punched five times, and the tissue was transferred to a cooled Dounce-homogenizer containing Nuclei EZ lysis buffer to homogenize the tissue. The samples were filtered through a 40 μm mini cell strainer followed by a 25%/29% iodixanol gradient to separate debris from the nuclei. To be able to distinguish samples later in post-sequencing analysis, the pellet was resuspended in FACS buffer containing sample-specific hash-tagged antibody recognizing a nucleus specific protein. A hashtag is an antibody with an oligonucleotide tail with PCR handle, a unique barcode and a poly A tail.The sample were stained with DAPI (0.5 µg/ml) and purified using the Sony SH800S cell sorter. Whole nuclei were gated using the DAPI fluorescence at 450±25 nm, to avoid debris, and sorted directly into RT Reagent B (10 X genomics) and further processed according to the company’s guide and sequenced using a NovaSeq 6000 (Illumina). Commercially available anti-nuclear pore complex proteins antibodies from BioLegends, Inc were used for hash-tagging. Per lane five different antibodies, to distinguish the different samples were used. Across the 5 sequencing lanes we used the same set of antibodies .Mus musculus (mm10) genome with default parameters. All libraries were converted to FASTQs using bcl2fastq. The FASTQ files were aligned to the mouse genome (assembly - GRCm38.p6) and subsequent read quantification was performed using the Salmon pipeline. Each library underwent separate quality control steps as described in the Alevin pipeline  functions  from the R package scuttle . Counts scuttle . The intSyp, Galntl6, Rbfox3, Syn1, Dnm1). Cells in clusters with high expression of these markers were classified as neurons and low expression as non-neuronal cells. The dataset was separated based on this classification and subsequently analysed independently. For identifying GABAergic and glutamatergic cells we used likewise a GMM based on known GABAergic and glutamate markers . Cells in clusters with high expression of GABAergic markers and low expression of the glutamatergic marker were defined as GABAergic cells and vice versa cells in cluster with high expression of the glutamatergic marker and low expression of the GABAergic markers were defined as glutamatergic. We divided the dataset further based on this classification. We identified a neuronal cluster expressing neither of the GABAergic nor glutamatergic markers. It also did not express any other neuron markers (histaminergic (HA), dopaminergic, ect.), and the cluster was therefore moved to the non-neuronal dataset.Cells were clustered into neuronal and non-neuronal cells using a Gaussian mixture model (GMM) based on the expression of pan-neuronal markers  neuronal populations were identified and classified based of known marker genes. Additionally, we used published datasets from the hypothalamus  to confiDESeq2 and scran R packages. Calculations were performed per time point. In order to account for biological variance we selected only cell types which were comprised of nuclei from at least three animals. Furthermore, we wanted to assure to only include cell types with a sufficient amount of nuclei (at least 9 nuclei/time point) and only included a cell type if it contained at least three nuclei from at least three individual animals. Further we selected only robustly expressed genes in a cell cluster for DGE analysis, meaning a gene must be expressed in at least 95% of the cells of one of the tested time points versus the control to be included in the analysis. The counts were aggregated using the Seurat function ‘AggregateExpression’ and transformed to a DESeq2 data set with the function ‘DESeqDataSetFromMatrix’. We calculated normalization factors using the scran function ‘computeSumFactors’. The differential expression analysis was performed using a likelihood ratio test (LRT) and a Gamma-Poisson Generalized linear model (glmGamPoi) for fitting the dispersion to the mean intensities. p Values were adjusted (for time points tested x number of cell populations x the number of genes) using the false discovery rate by Benjamini and Hochberg.To calculate gene expression fold changes in samples from infected mice compared to the control group, we used a pseudo-bulk approach. We calculated in total three different differential gene expression datasets; (I) Aggregating counts per gene across all cells without discriminating between cell types or clusters, (II) aggregating counts per gene for all GABAergic, glutamatergic, and non-neuronal cells and (III) aggregating counts per gene for each identified cluster. Differential gene expression analysis was performed used the Agrp/Npy+, Lepr+, Pomc+, Hcrt+, Pmch+, and Hdc+) we performed and additional differential gene expression analysis with adjusted parameters to identify DEGs also for small cell clusters  local dependencies between GO terms. For the t-test statistic we used in both cases a Fisher exact test. p-Values returned by the elimination method are considered as not affected by multiple testing and hence can be interpreted as corrected , we used the age in R . All sigorrected  . Enrich important study combines experiments and computational approaches to understand the effects of influenza H1N1 infection on hypothalamic cells. The methodology and analysis are solid and raise questions around how a respiratory virus affects the central nervous system.This  This study uses single-cell genomics and gene pathway analysis to characterize the transcriptional effects of influenza H1N1 infection on hypothalamic cell types. The authors use droplet-based single-nuclei RNA-seq to profile genome-wide RNA expression in adult mouse hypothalamic cells at 3, 7, and 23 days after intranasal infection with the H1N1 influenza virus. Through state-of-the-art and rigorous computational methods, the authors find that many hypothalamic cell types, glia, and especially neurons, are transcriptionally altered by respiratory infection with a non-neurotropic influenza virus and that these alterations can persist for weeks and potentially affect cell type interactions that disrupt function. For instance, microglia shift towards a pro-inflammatory molecular phenotype at 3 days post-infection, while astrocytes and oligodendrocytes significantly alter their expression of oxidoreductase activity genes and transport genes, respectively, at 7 days post-infection. In addition, POMC neurons of the arcuate hypothalamus, which suppress appetite and increase metabolism, appear to be unusually sensitive to H1N1 infection, upregulating more genes than other hypothalamic neurons. The authors' thorough discussion of the findings raises interesting questions and hypotheses about the functional implications of the molecular changes they observed, including the physiological changes that can persist long after acute viral infection. Given the role of the hypothalamus in homeostasis, this work sheds light on potential mechanisms by which the H1N1 virus can disrupt cell function and organismal homeostasis beyond the cells that it directly infects. The new work from Lemcke et al. suggests that the infection with Influenza A virus causes such flu symptoms as sleepiness and loss of appetite through the direct action on the responsible brain region, the hypothalamus. To test this idea, the authors performed singothalamus in controlled experimental conditions  and analyzed changes in the gene expression in the specific cell populations. The key results are promising and spurring future research. After revision, the analysis was considerably improved. Alternative approaches were used for testing. Specifically, during the revision: (1) The annotation of cell types was considerably improved; (2) The authors performed an additional analysis comparing case-control studies (Cacoa), where they could partly confirm their earlier findings. The following is the authors’ response to the current reviews.Reviewer #1 (Recommendations For The Authors):The revision and rebuttal have addressed all concerns raised in the initial review. Upon review of the revised figures, however, it is unclear why Figure 8C shows many significant DEGs in POMC neurons , whereas Figure 6A shows few to no DEGs in the GABA_24 cluster. Same for Pmch neurons/Glut_25, which seem to be missing from Figure 6A.Answer: In order to capture changes in these smaller cell population we performed an additional DEG analysis with modified and less strict parameters . We mention the different parameters in the methods part of the revised manuscript ).The following is the authors’ response to the original reviews.Reviewer #1 (Recommendations For The Authors):Major issues1. A key conclusion of this study is that neurons show longer lasting infection-related changes in gene expression than do non-neuronal cells, suggesting that neurons are more persistently affected, which could potentially underlie persistent effects of infection on behavior or physiology. However, the authors also report that over twice as many transcripts were captured in neurons than in non-neuronal cells, and that neurons and non-neurons were not equal in number. The number of transcripts and cells per cell type can affect the likelihood of detecting a differentially expressed gene when comparing cell types. Thus, the difference in infection related DEGs between non-neuronal cells and neurons may be due in part to differences in the numbers of transcripts and cells in each group. How would the number of infection related DEG's compare if the same number of transcripts were detected in neurons as in non-neuronal cells? In addition, is there any relationship between thenumber of infection related DEGs detected and the number of cells in the respective groups?We performed an additional analysis, down sampling the transcripts per cells to similar numbers (~1600 transcripts/cell), showing a similar pattern as shown in the original calculation of DEGs. High downregulation of genes in GABAergic, Glutamatergic and Nonneuronal cells at 3 and 7 dpi, but long-lasting dis-regulation at 23 dpi only in the neuronal subtypes. The analysis results can be found in Supplementary figure12 and on page 11 in the results section.1. The rationale for focusing on the LH and DMH is unclear. While these regions do play important roles in control of body weight and wakefulness, the authors do not report whether the cell types relevant to these functions are among those affected by infection. For instance, the authors mention HCRT and MCH neurons in the introduction but do not comment on whether these neurons show any significant changes after H1N1 infection in their analysis. Also, what about the POMC neurons or the Lepr+ DMH neurons? Knowing whether and how these body weight associated cell types are affected could help to connect the phenotypic  and molecular changes observed.We have added an additional analysis of some well know hypothalamic subtypes. What is interesting is that the different neuronal subtypes respond to the infection differently. While most neurons show the strongest response at 3dpi, POMC+ neurons show consistent changes across all three time points. This could point to different neuronal subtypes paying different roles in the sickness response to the influenza infection. The new data has been added to Figure 8 together with new text in the result section and discussion (Page 17 & 20).1. For discriminating neurons and non-neuronal cells based on their expression of neuronal marker genes, was this performed at the single-cell level or the cluster level? Similarly, was the discrimination of GABAergic and glutamatergic neurons done at the cell or cluster level?The discrimination of the cell types was done on single cell level. This information has been added to the revised manuscript on page 25.1. The authors mention that body weight did not change in some of the mice. Was there any difference in infection related DEGs between the mice that lost weight and the mice that didn't? Was there any correlation between the molecular and phenotypic  changes observed?We agree that this could have been an interesting point to investigate, however, we can only say with certainty for 2 animals in the recovery group (23.7 and 23.8) that they didn’t lose weight (Supplement figure 2). In Figure 4A we show that overall the different time points group well together, with exception for animal 23.7 which seems to have a better overlap with 7 dpi, indicating that we possibly captured here a delayed disease response. However, to make any indepth analysis, we have to few animals without weight-loss.5)The authors noted that the hypothalamic neurons continue to show infection-related changes in gene expression at 23dpi though body weight has returned to normal. In this H1N1 model, are there any persistent behavioral deficits at 23dpi that could be explained by the persistent changes in gene expression in DMH and LH neurons?https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6596076/) focus on cognitive long term effects of viral infections. Even though they did not include the here used H1N1 model, they included the PR8 strain, but didn’t report any long lasting behavioural or cognitive changes. So far only cognitive deficits during the acute phase of the infection caused by the PR8 H1N1 model have been shown. This would be a very interesting follow up study to perform, but this, we believe, is out of scope for the current manuscript.We did not test for long-lasting behavioural changes in these animals. Another study by Hosseini et al. , or could this be related to the infection ?We have added the location of the punching within the hypothalamus for the different groups to the supplements . The differences in neuron/non-neuron composition could originate from differences in the punching location, but we do not have data to support this conclusion. The difference could also stem from biological alterations during the infection.1. Since influenza viruses replicate in the cell nuclei, did the authors capture any H1N1 RNA in their single-nuclei RNA-seq samples?We mapped the single nuclei data against the viral genes, but could not detect any of the viral genes in the data set. We are still optimizing detecting of low amounts of viral genes in snRNA-seq data and have not included this information in the manuscript. We believe, that the virus did not manage to migrate in the hypothalamus and infiltrate the cells in the here captured area.Minor Issues1. Page 1. The abstract ends with the sentence: This is complemented by increased activity of microglia monitoring their surroundings. Presumably, the authors are basing this statement on the functions of genes altered in microglia by infection. However, saying that microglia behavior has changed is a bit of a stretch here, since the results suggest a change in the molecular phenotype of microglia but do not demonstrate a change in their behavior.We agree that the phrasing of the end of the abstract was not accurate and didn’t reflect the outcome of the analysis. We adjusted the sentence to: “The change of microglia gene activity suggest that this is complemented by a shift in microglia activity to provide increased surveillance of their surroundings.” Which should provide a better idea that the findings we present are a suggestion based on the transcriptomic changes in the cell population (Page 1).1. Page 8. The authors refer to Th+, Ddc+ neurons as dopaminergic. However, adrenergic/noradrenergic neurons also express these genes. How do the authors know the neurons are not adrenergic/noradrenergic?There are to our knowledge no nor-adrenaline/adrenaline producing neurons in the hypothalamus. In contrast dopaminergic neurons have indeed been identified in this area.1. In the Methods section, Slc17a6 and Slc32a1 are not "pan-neuronal markers" since they are only expressed by subsets of neurons.We removed the glutamatergic and GABAergic marker genes (Slc17a6 and Slc32a1) from the list of neuronal markers. They are stated further down in the method section as glutamatergic and GABAergic markers. Find the changes on (Page 24/25).1. Was the hashtagging antibody custom or commercial? If commercial, what was the source, catalog #, lot #? If custom, the authors should describe how it was made and validated.We used commercial antibodies for hash-tagging. We added the missing information to the manuscript and can be found on Page 24 of the revised manuscript.1. In the data processing section of the Methods, SCTransform is mentioned twice. Was normalization with SCTransform applied twice?The data was only normalized once using the SCTransfrom method. We adjusted the part of the method section to make it more clear (Page 24).1. In the section on gene set enrichment analysis, the first sentence includes this text: "(is a reference needed?)." The answer is yes - Alexa A, Rahnenfuhrer J (2022). topGO: Enrichment Analysis for Gene Ontology. R package version 2.50.0.The missing reference was added (Page 26).1. Page 4: "leaved" should be corrected to "left"The wrong wording was corrected.1. Figure 2D - gene is labeled as Slc31a1 on the figure and Slc32a1 in the figure legendWe provided a new Figure plate with the right marker genes.1. Official gene IDs should be italicizedWe checked the gene IDs again, and italicized wrongly formatted gene IDs.1. It is not clear whether the authors are planning to share their code. However, their code would be needed to reproduce their results, since the methods section provides a summary of what was done but lacks key details Code will be shared on request. We added this also to the revised manuscript (Page 1).Reviewer #2 (Public Review):The new work from Lemcke et al suggests that the infection with Influenza A virus causes such flu symptoms as sleepiness and loss of appetite through the direct action on the responsible brain region, the hypothalamus. To test this idea, the authors performed single-nucleus RNA sequencing of the mouse hypothalamus in controlled experimental conditions  and analyzed changes in the gene expression in the specific cell populations. The key results are promising.However, the analysis  is not optimal and incomplete and, therefore should be significantly improved.More specifically:1. The current annotation of cell types  did not make a good link to existing cell heterogeneity in the hypothalamus identified with scRNA seq in about 20 recently published works. All information about different peptidergic groups can not be extracted from the current version (except for a few). There are also some mistakes or wrong interpretations . This state is feasible to improve (and should be improved) with already existing data.We repeated the cell label transfer with the newly published HypoMap and added additional information to the supplements about the cell type assignments. Additionally, we agree that the dopaminergic neurons do not belong to the group of glutamatergic neurons, however assigned them into this group based on the clustering. We changed the phrasing in the results, to make a better differentiation between the two groups (Page 8).1. I am confused with the results shown in the label transfer  applied to some published datasets (authors used the Seurat functions 'FindTransferAnchors' and 'TransferData'). The final results don't make sense: while the dataset for the arcuate nucleus  well covered the GABAergic neurons it is not the case for the whole hypothalamus datasets . Similarly, for glutamatergic neurons. Additionally, I could not see that the label transfer works well for PMCH cells which should be present in the dataset for the lateral hypothalamus .We performed the additional label transfer of the hypothalamus data. Here we accepted a prediction score of 0.5 and transferred a cell type label to our annotated cluster IDs, if at least 10% of cells within a cluster were annotated with the 0.5 prediction score. We found that well defined neuron population types like Hcrt+, Pmch+ and Hdc+ neurons as well as Pomc+ neurons were tagged with a high predictions scores  and non-neuronal cell types  were well annotated. Additionally we identified an Agrp+ neuron population with the Gaba_1 neurons. This information has been added to the revised manuscript .1. There are newly developed approaches to check the shifts in the cell compositions and specific differential gene expression in the cell groups . Therefore, I did not fully understand why here the authors used the pseudo-bulk approaches for the data analysis . Therefore it would be great to use at least one of those approaches, which were developed specifically for the scRNAseq data analysis. Or, if there are some reasons - the authors should argue why their approach is optimalWe performed an additional analysis comparing case-control studies (Cacoa). We perfomed both modalities, cluster-based and cluster-free expression shifts and cell type compositions We could partly confirm our findings using the pseudo-bulk approach. The clusterspecific density shift  identified only shifts in non-neuronal cell types between the Control group and 3 dpi. We believe, these composition shifts are caused by the lower number of non-neuronal cells in the 3 dpi time point. Cluster-specific expression shifts show similar results as in the pseudo-bulk approach, with significant expression shift identified at 3 and 7 dpi in neuronal and non-neuronal cell clusters . However, no significant expression shifts were identified in the recovery group at 23 dpi. Using the cluster-free expression shift approach, however we were able to identify a similar picture as described with the pseudo-bulk approach. In the recovery group at 23 dpi, we found mainly changed gene programs in neuronal cells, and no transcriptional changes in the non-neuronal cells . This new analysis has been added to the revised manuscript  including supplementary figure and tables as stated.1. When the authors describe the DGE changes upon experimental conditions , my first comment is again relevant: it is difficult to use the current annotation and cell type description as the reference for testing virus effects and shifts in the DGE in distinct neuronal subtypes.The cell type annotations have been checked and additional label transfer has been performed. All figures in the manuscript has been updated.I have to note that the experimental design is well done and logical. Therefore I believe that to strengthen the conclusions, the already obtained datasets can be used for improved analysis.Reviewer #2 (Recommendations For The Authors):I have some minor concerns:1. For the quality check it would be good to see how different hashed samples for each timepoint cover the UMAP embeddings.We added the UMAP embeddings to the supplement .1. In Fig 1e colors are not optimal - it is impossible to assess it.We separated the UMAPs for the different time points to make it easier to assess. See updated Figure 1E.1. In the methods authors started "Single-nucleus RNA-sequencing cell population identification" from the description of using a Gaussian mixture model (GMM). However, I could not clearly understand how this model was used and which kind of result it provided.We used an GMM model with known markers for neurons and in a second step for glutamatergic and GABAergic cells to sub-cluster the cells and then selected based on high and low expression of the marker genes in the cluster into their respective classes. This information has been added to the method section (Page 24/25).1. Could the authors better clarify why "they calculated normalization factors using the scran function 'computeSumFactors'" when working with pseudobulk analysis?This size factor normalization was recommended for single cell data by the authors of the DESeq2 packages.http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html1. I didn't find logic in "a cell cluster was only included if it contained more than 2 nuclei in at least 3 individual animals" (page 24). Maybe I misinterpreted it.The rationale for the selection methods was based on the findings that not all animals in the recovery group had the same effects in weight loss. The acute time points didn’t show enough weight loss to decide if all animals in these groups lost the same amount and were equally sick. Hence, in order to have biological robustness we decided to only analyse clusters where cells from at least 3 animals at a specific time point contributed to a cell type. In order to have enough cells per cell type for the calculation of DEGs, we decided to only include a cell type at a specific time point if it contained at least 3 cells from one individual. This selection method limits the analysis to cell types with at least 9 cells per time point.


Access to services remains the biggest barrier to helping the most vulnerable in the South African Stuttering Community. This novel stuttering therapy, harnessing an unconscious link between eye and tongue movement, may provide a new therapeutic approach, easily communicated and deliverable online.This study provides both objective and subjective assessments of the feasibility of this intervention. Assessment tools holistically address all components of stuttering in line with comprehensive treatment approaches: core behaviours, secondary behaviours, anticipation and reactions.On receipt of ethical approval, this single-subject case design recruited one adult (21-year-old) male with a developmental stutter (DS). The participant gave informed consent and completed four scheduled assessments: baseline, after 5-week training, 3 months post-intervention and 24 months post-completion. The study used objective assessment tools: Stuttering Severity Instrument-4 (SSI-4); Subjective-assessment tools: SSI-4 clinical use self-report tool (CUSR); Overall Assessment of Speaker’s Experience of Stuttering (OASES-A); Premonitory Awareness in Stuttering (PAiS) and Self-Report Stuttering Severity* (SRSS) .The participant’s scores improved across all assessment measures, which may reflect a holistic improvement. The participant reported that the tool was very useful. There were no negative consequences.This case report indicates that this innovative treatment may be feasible. No adverse effects were experienced, and the treatment only benefited the participant. The results justify the design of a pilot randomised feasibility clinical trial.The results indicate that this is a needed breakthrough in stuttering therapy as the instructions can be easily translated into any language. It can also be delivered remotely reducing accessibility barriers. This descriptive case report explores whether making a specific, timed, downward eye movement is effective as a holistic intervention for a young adult man with a developmental stutter (DS). This novel self-help intervention aims to enable the person who stutters to circumvent an inadvertent block or repetition, should they choose to do so. It harnesses an unconscious synchronised linking of eye movement and the posterior of the tongue, discussed by the main authors in a previously published paper  and non-action  responses in the anticipation of stuttering  lockdown. Stuttering severity was also self-assessed by the participant. Additionally, the participant was asked to assess their anticipation of stuttering events, the overall experience of being someone who stuttered and any changes in the frequency of avoidance behaviours and perceived locus of control. The data presented reflect the participant’s ability to use the technique in real life over the 2 years following the 5-week instruction period.The approach used in this study is to familiarise the participant with the process of using a conscious eye movement to change their posterior tongue position before commencing utterance of the next speech sound. During training, it is just this movement that is practised and the timing of it discussed. The required positioning in the back of the tongue occurs when they move their eyes downwards towards their naval. This ‘ideal’ tongue position is with the posterior of the tongue raised and as far back in the oral cavity as possible. Having the tongue positioned on the roof of the mouth has been shown to improve muscle strength  for core and secondary behaviours .To describe the scores obtained on the Overall Assessment of the Speaker’s Experience of Stuttering-Adults (OASES-A) regarding its influence on activity limitations, participation restrictions and contextual factors  .To describe any changes to the self-reported stuttering severity, avoidance behaviours and locus of control measures assessed through the SSI-4 auxiliary Clinical Use Self-Report (CUSR). These measures reflect the ICF guidelines regarding participation, avoidance behaviours and perceived locus of control .To describe the scores obtained on the Premonitory Awareness in Stuttering (PAiS) with regard to immediate and prospective anticipation .To compare results provided on self-rated stuttering severity (SRSS) with SSI-4 scores. This measure was included only at 24 months post-intervention assessment (Phase 5).This study used a single-subject case design including assessments at four time periods  and a 5-week intervention period see .The participant was a right-handed male Caucasian, age 21 years, who was an undergraduate student at the Institute of Technology in Sligo, Ireland. He does not know at what age he started to stutter, just that he has stuttered for as long as he can remember. He had participated in some classes for speech in childhood without much success, and at age 19, he took part in an intensive 3-day training with the Maguire programme: a self-help programme run by PWS. He still uses the techniques he learned from the programme but finds them hard to remember. He was proficient in English and had no diagnosis, needing the psychological or medical intervention of any emotional, behavioural, learning or neurological disorder. He had the cognitive ability to take part and gave informed consent.The tools used at the four testing periods  were SSI-4, OASES-A and PAiS. The SRSS was added at 24 months follow-up (Phase 5).The SSI-4 is the most widely used syllable-based procedure for assessing overt stuttering symptoms Riley, , 2009. TThe minimum detectable change (MDC) in the percent of stuttered syllables (%SS) has been set as 1%  required participants to score their typical SRSS for eight individual representative speaking situations, on a scale of 1–9 (where 1 = no stuttering and 9 = extremely severe stuttering) that represents typical, best and worst stuttering severity in that situation. The mean of the eight scores was used in the analysis. This tool was added to the assessment at phase 5 to assess whether an expert in stuttering’s self-assessment, concurred with the final independent assessment of stuttering severity. Minimum clinically important difference and MDC can be approximated based on established measures for SSI-4 using the comparison table published by Jones et al. (2004).Phase 2: Eye movement training for stuttering intervention: The intervention was delivered via Zoom, at the start of the COVID-19 pandemic. The intervention took place over a 5-week period. A practice Zoom session was conducted prior to commencing the intervention schedule. Total contact time for participant and researcher over five weeks training, was 1 h 17 min (excluding time spent on assessments).During meeting one, the participant was trained on how to use eye/tongue movement to produce non-stuttered speech. The participant was instructed to practise this movement when he experienced dysfluencies for 10 min twice per day for the next week. As a result of the lockdown, the participant did not experience stuttered speech at home alone or with family during the first week. Based on this feedback, the practice schedule was adapted in the second week, initially to an online practice schedule with the principal researcher and then back to self-directed, as it became apparent that the movement could be practised in the absence of stuttered speech.The remaining online sessions were support sessions where the participant confirmed that he had complied with the practice schedule and the precise timing of the movement was discussed: if a stuttering-like moment started, stop talking, reposition the tongue and start again.Key instructions from training sessions:Attention was brought to linked eye–tongue movement – non-vision related as occurs with eyes closed.Role of eye movement in unconscious predicting movement was discussed.Ideal tongue position was discussed – practice to reposition quickly – like a blink.Consider the timing of movement – before speech gesture.The participant was reminded that this was a tool he could use if he wished to avoid a stuttering-like moment and that it would get easier with practice. It was not something he had to do, but something he could do if it helped.First-week instructions were to practise with speech, second and subsequent weeks – no speech was required to practise eye–tongue movement.Responses from SSI-4 and OASES-A were analysed based on total scores, impact scores and severity ratings using descriptive statistics. The CUSR adjunct to the SSI-4 was analysed by creating composite scores assessing self-rated stuttering severity, avoidance behaviours and locus of control , the Institute of Technology Sligo  and the Sligo University Hospital Research Ethics Committee (reference no.: 800).The results are presented to reflect the five study objectives described in the ‘Methods’ section.To describe the SSI-4 scores for core and secondary behaviours see .The SSI-4 scores  at the fTo describe the scores obtained on the Overall Assessment of the Speaker’s Experience of Stuttering – Adults (OASES-A) with regard to its influence on activity limitations, participation restrictions and contextual factors .The OASES-A scores and categorisation  improvedSection 1  did not improve overall, maintaining a moderate-severe negative impact see . This seSection 2  improved from moderate–severe (3.4) to mild–moderate (2.1) at Phase 3 and then stabilised at moderate (2.8) at phases 4 and 5 see . The finSection 3 (Communications in Daily Situations) improved from moderate to mild–moderate at phases 3, 4 and 5. Within the range of this category, the initial % change from baseline was 20% (Phase 3), which decreased to 17% at Phase 4 and again to 10% at Phase 5.Section 4  mirrored the change in the overall OASES-A score, improving from moderate–severe to mild–moderate. Four of the 25 measures in this sub-section remained at returned to baseline by Phase 5; however, 21 of the 25 measures assessing the participant’s Quality of Life improved. This was not a continual improvement. The participant’s score had reduced by 40% from baseline at Phase 3, it was 21% at Phase 4, and it had 37% change from baseline at Phase 5 see .To describe the SSI-4 CUSR measures. Composite scores of self-reported stuttering severity, avoidance behaviours and locus of control measures were assessed through the SSI-4 to reflect the ICF guidelines regarding participation and avoidance behaviours as well as the perceived locus of control.The participant self-reported  that hisTo describe the scores obtained on the PAiS with regard to total scores and immediate or prospective anticipation scores. Coinciding with an international increase of interest in the role of anticipation in the stuttered speech, the PAiS  was the largest contributor to the overall reduction in PAiS score having decreased 70% from baseline at Phase 5.To compare results provided on self-rated stuttering severity (SRSS) with SSI-4 scores. This measure was included only at 24 months post-intervention assessment (Phase 5).The participant rated his satisfaction with speech at 2 on a scale of 1–9 with 1 = extremely satisfied and 9 = extremely dissatisfied. He rated their level of avoidance at 1.25 on a scale of 1–3 with 1 = rarely and 3 = most of the time. He also rated their typical worst and best severity on a scale of 1–9 with 1 fluent and 9 extremely severe stuttering. These scores are converted to comparative %SS based on published analysis  than self-assessed severity (12.6% approximately) and when reading the actual performance was better (0.53%) than self-assessed best (5% approximately). The average of those scores is 8.54% corresponding well with the SRSS typical severity (8.6% approximately). This supports the use of typical SRSS when expanding the research.The discussion elaborates on the objectives of the study and their respective findings.To describe the scores obtained on the SSI-4 for core and secondary behaviours.The objective assessment of the participant’s Severity Ratings implied an improvement from moderate to very mild at the final assessment. It is important to recognise that only unconscious tongue position (linked with unconscious eye movements) was potentially modified or targeted in this intervention. While the intervention did not specifically deal with modifying speech in any way, objective assessment reported the participant reduced %SS, duration of longest dysfluency, physical concomitants and SSI-4 total scores (> 50% reduction from baseline). The %SS in reading task decreased by 97%. In the speaking task, the reduction was 46% at the final assessment. This difference is reflective of the inherent variability of stuttering severity across different contexts: online assessment, reading versus speaking tasks, social settings, etc., and would suggest that the eye movement enables fluent speech but is more difficult to use when trying to maintain eye movement for communication in social situations. %SS is presented rather than the scaled SSI-4 score because of its lack of sensitivity as a tool. Initial improvements in %SS were not reflected in a change in the SSI-4 verbal category because of the skewed nature of the population of PWS. Assessments between Phase 1 and Phase 3 show a 10.9% reduction in %SS, but the scaled SSI-4 score (17) would have remained the same see .While it has been reported that PWS are prone to relapse in the absence of booster therapy, the participant’s scores continued to improve across the four testing phases, with continued self-improvement in the 24 months before the final assessment, without any additional support events. In addition, while secondary behaviours were not specifically targeted, there was a reduction in physical contaminants, duration of dysfluency and increased speech naturalness across the four testing periods. This again implies unconscious tongue positioning plays a role in the subsequent chain of stuttering events for PWS.By 24 months follow-up, the negative impact of being someone who stutters, as assessed with OASES-A, had been reduced from moderately severe to mild–moderate, with a 29% total reduction in scaled score from baseline. This scale is seen as a good indicator of therapy outcomes, more so than stuttering severity, and is less subject to variability Yaruss, . TherefoGeneral Information score was reduced initially from moderate–severe to moderate, but this had reverted to moderate–severe at Phase 5. Speaker’s Reactions score reduced by 39% initially to mild but stabilised at mild–moderate (19% improvement) indicating they did not have as many negative reactions as before the intervention. Daily Communication improved from moderate to mild–moderate, although this decreased across phases 3–5 (from 20% to 10% improvement).These results are comparable to those reported following a comprehensive stuttering intervention  within SSI-4. The participant self-reported that their average stuttering severity had improved with a reduction of 19% from the baseline score, his frequency of avoidance behaviours decreased initially by 19% and then further improved to 24% (phases 4 and 5), and he had an overall increased internal locus of control with a 31% change in score from baseline. In line with the ICF components these measures reflect improved function (19% change from baseline) reduced activity limitations and participation restrictions (24% change from baseline) and improved personal and environmental factors (31% change from baseline).The CUSR has a high test–retest reliability, and there was no significant correlation between any of the three CUSR items and the three main parameters within the SSI-4  to 3 (frequently). The participant said he rarely avoided seven out of the eight speaking situations. This correlates to the reduction of frequency of avoidance behaviours assessed through the SSI-4 CUSR and supports the validity of the measures used in this report.The participant rated their satisfaction with speech as 2, with 1 being extremely satisfied and 9 extremely dissatisfied. This was at Phase 5. The participant’s concluding comments regarding the technique, 24 months after participating were ‘Found it very useful. Is difficult to remember to do it but when I remember it really helps’. The total participant contact time was less than 2 h over a 5-week period online and 5 weeks of daily text messages. This is especially relevant as, as far as challenges to practice are concerned, COVID-19 has significantly increased the workload of clinicians , so that causality can be determined, and the results are more generalisable to the stuttering community. The link between non-vision-related eye movement and tongue position needs more scientific exploration as conscious eye movement may be a valuable clinical tool for stuttering.


The safety and efficacy of 17-gauge needles used in CT-guided percutaneous cryoablation for lung nodules were explored in this study. The purpose of the study was to compare the findings with earlier research and multi-center clinical trials that used various needle sizes.Between 2016 and 2020, a retrospective study was conducted with approval from the institutional review board. A total of 41 patients were enrolled, and 71 lung nodules were treated in 63 cryoablation procedures using local anesthesia. Complication rates were recorded, and overall survival rates as well as tumor progression-free rates were calculated using the Kaplan-Meier method.Self-limited hemoptysis was caused by 12.9% of the procedures, and drainage was required for pneumothoraces resulting from 11.3% of them. The overall survival rates at one, two, three, and four years were 97%, 94%, 82%, and 67%, respectively. The tumor progression-free rates at one, two, three, and four years were 86.2%, 77%, 74%, and 65%, respectively.Cryoablation for lung nodules using 17-Gauge needles can achieve similar rates of survival and tumor control rates, similar or even lower complication rates as compared with other studies and multi-center trials using mixed sized needles. For patients with a history of cancer who develop new lung nodules, surgical resection is frequently considered for both pathological evidence and therapeutic benefits –6. Some The Institutional Review Board (IRB) of our hospital granted approval for this investigation . Patients who underwent cryoablation (CA) at our hospital between 2016 and 2020 were included in this retrospective study. An signed agreement was obtained from each patient which documented a thorough explanation of the limitations and potential risks associated with CA. The limitation are : CA is a local regional treatment and there is possibility of residual or recurrent tumor. The risks discussed encompassed pneumothorax, hemoptysis, pleural effusion, needle tract tumor seedings, procedure-related frostbite, infection, and fistula formation.Patients were referred for CA for management of lung nodules from chest surgeon, when not eligible for surgical treatment. Theare are certain conditions that surgery is not recommended such as insufficient pulmonary function, personal refusal by the patients, or the surgery is excluded by the guidelines. The process of shared decision making was well-documented in the medical records. Patients who were referred for this study had a medical history of confirmed primary cancers and presented with newly developed or growing lung nodules, as observed in three or more sequential computed tomography (CT) studies. Some patients had undergone prior surgical removal of pre-existing pulmonary nodules. The indications for referrals and the demographic information of the enrolled patients are summarized in Table The exclusion criteria were: ECOG performance status score equal to or more than 3, uncontrollable bleeding tendency with international normalization ratio (INR) more than 1.5. The patients who were not capable to hold his/her breath or hold still position. Evidence of progression of primary or metastatic tumor outside the lung; Patients with medical evidence of active infection.In this study, all cryoablation procedures were performed in the CT scanning room. Patients receiving antiplatelet therapies such as Aspirin or Clopidogrel  were required to discontinue these medications 7 days prior to the cryoablation procedure. Local anesthesia was administered using 10-20mL of 2% Lidocaine injection. We utilized 17-gauge cryoablation needles  for the procedures. The positioning of patients varied between supine, prone, or decubitus positions to optimize the insertion route of the needles. CT scans were performed using a Philips brilliance 64 scanner . A non-contrast enhanced CT scan with a helical mode was initially conducted, with a 5 mm slice thickness and no gaps, at the start of the cryoablation procedure. This was followed by a 3 mm slice thickness after the CA needle was punctured through the skin, utilizing a tube voltage of 120Kv and a current of 89mA. The cryoablation procedure consisted of a freezing phase of 3 min, followed by a passive thawing phase of 3 min. This was then repeated with a freezing phase of 7 min, a passive thawing phase of 7 min, and finally a freezing phase of 10 min, followed by a 3-minute active thawing phase. CT studies of the entire lung were conducted prior to the cryoablation procedure (pre-CA CT), followed by CT studies of the ablation region after each thawing cycle to monitor the evolution of the iceballs. A post-procedure CT of the entire lung was performed after all the needles were removed. Our criterion for adequate cryoablation was to keep the tumors within a 5 mm margin of the minus 40 Celsius ablation zone, as specified in the cryoablation needle user manual published by the manufacturer . The cryoablation procedure was performed by a board-certified radiologist with 20 years of experience in cross-sectional intervention (WCL).Complications such as pneumothorax or hemoptysis were recorded by chart during and after the procedure. The criteria for pneumothorax is the presence of any newly found radiolucency between pleura and the lung parenchyma during and after CA procedure by CT. Hemoptysis was recorded on our Access database  when coughing with bloody sputum despite its amount was found during procedure. Any complaints by the patients during the CA procedure was also recorded on our Access database. Chart review was done for a period of 30 days following discharge to look for any documented discomfort or finding that may fall in the criteria by Common Terminology Criteria for Adverse Events (CTCAE)v3.0. The days of hospitalization was defined as the date of discharge minus the date the patient was admitted to ward.Follow up CT imaging after the CA procedure was done at three months interval. The 3-month post-cryoablation CT served as the baseline study for subsequent CT comparisons. This follow up protocol is in accordance with Taiwanese National Health Insurance Reimbursement Standards, which is also similar to the protocols mentioned in the ECLIPSE trial and in other articles . The scaWe used SPSS software 26.0 for statistical evaluation of outcome, patient demographics, and complication calculation. We have recorded continuous variables in mean, standard deviation (S.D.), minimal (MIN) and maximal value (MAX). The CA procedure time was defined as the time of first CT image was scanned to the time of procedure whole lung CT was scanned. The days of hospital stay were recorded as the interval between the patient was admitted and discharged, the days patient have spent in the hospital after the CA procedure were also calculated and recorded the way mentioned in SOLSTICE study .Kaplan Meier method in the survival analysis function was chosen to calculate survival rates and tumor progression free rate at time points of 12 months, 24 months,36months and 48 months after CA procedure. In Kaplan Meier Methods, 95% confidence interval was chosen in the option list. The period of survival was calculated from the day of the first ablation procedure to time of death.In the Kaplan Meier method, death by a direct or indirect consequence of lung cancer is designated as event as in the survival rate analysis and the patient who are still alive or loss of follow up were censored according to the chart review. The period of tumor progression free was calculated as the time from CA procedure to local failure. The tumor local failure was designated as event and loss of follow up was censored in the tumor progression free study, and the patients without progression were censored at the time of death or at the date of their last visit. The dates of the last OPD visit were updated until the end of 2020.Between Jan 2016 and July 2020, a total of 41 patients  were recruited for CA procedure. Five patients with 6 targets were excluded from survival and local tumor control study, four patients were excluded as a consequence of their referral for tumor debulking, and the remaining two patients were lost in follow up study without base line post procedure CT. Out of the 41 patients, 9 did not undergo systemic therapy due to factors such as advanced age, personal choice, and impaired renal function. The treated nodules can be considered as metastases from primary lung cancers , or from malignancies other than lung  because these patients had previous pathology proved lung metastases. Also there were patients were treated with CA for they had prior cancer history with progressive growing lung nodule (2 colon cancers and 4 lung cancers). The characteristics of the patient involved were summarized in Table Technical success was accomplished in 60/62(96.7%) procedures. Technical success was not achieved in two patients with two targets in each patient. Rapidly evolving pneumothorax during procedure was accountable for the failure to form the complete coverage of the target tumors in one patient. These four targets from the technical failures all contributed to local failure in the follow up.For immediate and delayed complications of CA procedure, pneumothorax was found in 32 out of 62(52%) procedures while 7 out of 32 procedures require pigtail drainage. Hemoptysis was found in 8 out 62 procedures (12.9%), which were all self-limited and the hemoptysis ceased during or after procedure without further management. No delayed complication or discomfort such as fever, infection, local paresthesia, skin frostbite, fistula or focal hematomas was complained according to the chart review between the date one day after CA procedure and the date of his or her first OPD follow up which usually took place one or two weeks after discharge from ward ,3 showed partial response (PR),27 showed stable disease (SD) 14 tumors were found to have local failure in 67 tumors,  , 25–28. Secondly, in this study we have shown the tumor progression free rates were 86.2% at first year, 77% at second year. According to the statements mentioned in the studies by de Baere et al., direct comparison for local tumor control rate is difficult between different studies . In thisIn addition to the relatively larger sizes of our targets, the number of second year tumor progression free rate can be up to 83%, if the four targets which are not categorized as technical success were not included in the progression free period calculation. In the fact, the four targets which were not treated with technical success all ended up with local failures. Since the ECLIPSE study has reported 100% technical success rate and the SOLSTICE study has reported the 97.2% without further description of the outcome, more evidence may be required to clarify the relationship between technical success and the local tumor control in CA procedures , 27, 28.In this study, we observed a pneumothorax rate of 52% per procedure, which is consistent with findings from other studies summarized in Table The second and final complication encountered in our cryoablation procedures was self-limited hemoptysis, which occurred in 12.9% of cases. This rate is lower than the reported rates of hemoptysis during and after cryoablation in previous studies, which ranged from 36.8 to 40%. This difference may be attributed to the use of a relatively smaller cryoablation needle (17-gauge) compared to the 12-16-gauge needles used in other studies –28, 32. It is important to note the limitations of this study. It is a retrospective study with a single-arm design, and the number of enrolled patients is limited. The study population is heterogeneous, consisting of patients with different primary tumor origins and varying tumor sizes. The impact of systemic therapy before and after the cryoablation procedure was not taken into account, which may influence the evaluation of therapeutic effectiveness. Additionally, the study lacks long-term data on tumor control rates beyond five years, requiring further observation.In conclusion, cryoablation using 17-gauge needles can achieve similar survival and tumor control rates, as well as comparable or lower complication rates, compared to multi-center trials that use various needle sizes.