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State-of-the-art methods for single-cell RNA sequencing (scRNA-seq) Differential Expression Analysis (DEA) often rely on strong distributional assumptions that are difficult to verify in practice. Furthermore, while the increasing complexity of clinical and biological single-cell studies calls for greater tool versatility, the majority of existing methods only tackle the comparison between two conditions. We propose a novel, distribution-free, and flexible approach to DEA for single-cell RNA-seq data. This new method, called ccdf, tests the association of each gene expression with one or many variables of interest (that can be either continuous or discrete), while potentially adjusting for additional covariates. To test such complex hypotheses, ccdf uses a conditional independence test relying on the conditional cumulative distribution function, estimated through multiple regressions. We provide the asymptotic distribution of the ccdf test statistic as well as a permutation test (when the number of observed cells is not sufficiently large). ccdf substantially expands the possibilities for scRNA-seq DEA studies: it obtains good statistical performance in various simulation scenarios considering complex experimental designs (i.e. beyond the two condition comparison), while retaining competitive performance with state-of-the-art methods in a two-condition benchmark. We apply \texttt{ccdf} to a large publicly available scRNA-seq dataset of 84,140 SARS-CoV-2 reactive CD8+ T cells, in order to identify the diffentially expressed genes across 3 groups of COVID-19 severity (mild, hospitalized, and ICU) while accounting for seven different cellular subpopulations.

This study aimed to evaluate the effect of probiotic (Saccharomyces cerevisiae boulardii CNCM I-1079) supplementation during feedlot adaptation period on performance of Nellore bulls. One-hundred ninety-eight Nellore bulls were used in a completely randomized block design, divided in 22 pens (9 animals/pen). Treatments were: CON – adaptation diet and finishing diet comprised of basal diet (with 28 ppm of monensin); and SCB – same diets as for the previous treatment with monensin plus probiotic (Saccharomyces cerevisiae boulardii CNCM I-1079, ProTernative® 20; Lallemand Animal Nutrition at a dose of 1.0 x 10e10 cfu/animal/d) supplementation. Supplementation was offered for the first 42 days (21 days in the step-up phase and 21 days in the finishing diet with 900 g concentrate/kg DM), then both treatments were fed the same basal diet for another 76d, reaching 118 days in total. Pen was considered the experimental unit and data were analyzed using the MIXED procedure of SAS with Tukey test at 5% significance level. Final carcass weight was 1.69% greater for animals fed SCB (283.8 vs. 288.7 kg, P = 0.04). Total carcass weight gain (111.0 vs. 114.8 kg) and daily carcass weight gain (0.924 vs. 0.956 kg) tended (P = 0.06) to increase by 3.46% in SCB-fed animals compared to the CON ones. Gain yield (P = 0.39), biological efficiency (P = 0.35) and carcass yield (P = 0.71) did not differ between treatments. Probiotic supplementation with live yeast during the first 42 days in feedlot helped Nellore bulls optimize early on growth performance with an interesting carry-over effect on carcass gain through the total feedlot time.

High morbidity and mortality rates of calves dramatically impact the sustainability of the dairy industry. Probiotics are investigated for their potential to alleviate health challenges. The objective of the study was to determine the effect of live yeast Saccharomyces cerevisiae var. boulardii CNCM I-1079 (SB) supplementation in pre-weaning calves. Thirty Holstein calves were enrolled at d6 of age and were offered daily 8 L of milk replacer with ad-libitum access to water and pelleted starter. Calves were balanced in term of parity of the dam and birth weight and randomly assigned to two experimental groups, designated as supplemented and Control. Supplemented calves received 2x109 cfu/d of SB added in the milk replacer. Every abnormal health condition, such as gut and respiratory disorders, as well as therapeutic treatments were recorded daily for each animal. A linear mixed model was performed to assess the impact of the supplementation on the different variables measured. Two fecal samples were collected at d10 and d60 of the trial to analyze bacteria diversity and taxonomic composition by amplicon sequencing. The overall health was improved in SB calves with fewer diarrhea cases detected compared with Controls (0.7 vs 2.2/calf, respectively, P = 0.001) and fewer SB calves experiencing respiratory disease (6.3 vs 50% respectively, P = 0.090). Along with those observations, a lower number of SB calves received a therapeutic treatment (18.8 vs 62.5% respectively, P = 0.029). Consequently, SB calves weighed 3 kg more compared with the Controls at the end of the experiment (P = 0.087). SB calves significantly presented a greater relative abundance of Akkermansia, a genus involved in immune system maturation, and a greater proportion of Lactobacillales and Negativicutes (lactate users) indicating likely better adaptation of their fecal microbiota to the milk diet than in Controls.

SummaryState-of-the-art methods for single-cell RNA sequencing (scRNA-seq) Differential Expression Analysis (DEA) often rely on strong distributional assumptions that are difficult to verify in practice. Furthermore, while the increasing complexity of clinical and biological single-cell studies calls for greater tool versatility, the majority of existing methods only tackle the comparison between two conditions. We propose a novel, distribution-free, and flexible approach to DEA for single-cell RNA-seq data. This new method, called ccdf, tests the association of each gene expression with one or many variables of interest (that can be either continuous or discrete), while potentially adjusting for additional covariates. To test such complex hypotheses, ccdf uses a conditional independence test relying on the conditional cumulative distribution function, estimated through multiple regressions. We provide the asymptotic distribution of the ccdf test statistic as well as a permutation test (when the number of observed cells is not sufficiently large). ccdf substantially expands the possibilities for scRNA-seq DEA studies: it obtains good statistical performance in various simulation scenarios considering complex experimental designs (i.e. beyond the two condition comparison), while retaining competitive performance with state-of-the-art methods in a two-condition benchmark. We apply ccdf to a large publicly available scRNA-seq dataset of 84,140 SARS-CoV-2 reactive CD8+ T cells, in order to identify the diffentially expressed genes across 3 groups of COVID-19 severity (mild, hospitalized, and ICU) while accounting for seven different cellular subpopulations.

This study aimed to evaluate the effect of probiotic (Saccharomyces cerevisiae boulardii CNCM I-1079) supplementation during feedlot adaptation period on performance of Nellore bulls. One-hundred ninety-eight Nellore bulls were used in a completely randomized block design, divided in 22 pens (9 animals/pen). Treatments were: CON – adaptation diet and finishing diet comprised of basal diet (with 28 ppm of monensin); and SCB – same diets as for the previous treatment with monensin plus probiotic (Saccharomyces cerevisiae boulardii CNCM I-1079, ProTernative® 20; Lallemand Animal Nutrition at a dose of 1.0 x 10e10 cfu/animal/d) supplementation. Supplementation was offered for the first 42 days (21 days in the step-up phase and 21 days in the finishing diet with 900 g concentrate/kg DM), then both treatments were fed the same basal diet for another 76d, reaching 118 days in total. Pen was considered the experimental unit. Growth data were analyzed using the MIXED procedure of SAS with Tukey test at 5% significance level, and a linear regression analysis was performed for the dry matter intake (DMI) data. During the first 42 days, average DMI did not differ; however, bulls fed SCB plateaued the curve of at 9.17 kg DMI/d earlier (39 days, R2 = 0.97) than those fed CON (43 days; R2 = 0.96) diets. Final body weight (509.5 vs. 518.0 kg, P = 0.02), total body weight gain (163.7 vs. 170.3 kg, P = 0.01) and average body weight daily gain (1.366 vs. 1.420 kg, P = 0.01) were greater for SCB-fed bulls. Feed efficiency of SCB-supplemented bulls was 8.05% greater than CON (P = 0.04). Probiotic supplementation with live yeast Saccharomyces cerevisiae boulardii CNCM I-1079 during the first 42 days in feedlot contributes to reaching the plateau of DMI curve earlier and improving overall performance of Nellore bulls during the total period.

RNA-seq studies are growing in size and popularity. We provide evidence that the most commonly used methods for differential expression analysis (DEA) may yield too many false positive results in some situations. We presentdearseq, a new method for DEA which controls the FDR without making any assumption about the true distribution of RNA-seq data. We show thatdearseqcontrols the FDR while maintaining strong statistical power compared to the most popular methods. We demonstrate this behavior with mathematical proofs, simulations, and a real data set from a study of Tuberculosis, where our method produces fewer apparent false positives.

Session 2: Microbial ecosystem and herbivore nutritionSession 2: Microbial ecosystem and herbivore nutrition; Changes in rumen metabolites from lambs fed a feed additive containing linseed oil and garlic essential oil. 10. International Symposium on the Nutrition of Herbivores (ISNH10)

This study aims to assess the cumulative risks and impacts of all REDD+ initiatives in Mai-Ndombe on the rights and subsistence of local communities and Indigenous Peoples, using existing tools while taking into account gray areas of the REDD+ process. Findings come from existing project documentation, field studies conducted in recent years, and a series of interviews with REDD+ stakeholders in Mai-Ndombe. The study provides a mapping of all existing and planned REDD+ initiatives in the province, as well as a cross-cutting contextual analysis of risks which connects REDD+ to human rights. This is followed by an assessment of these initiatives’ cumulative impacts as well as of national and project strategies to address and reduce risks. It thus offers a perspective on the link between the accumulation of REDD+ initiatives and conflicts at different scales.