Table 7_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

“…In another experimental setting with tulathromycin treatment, bacterial abundance quantification by flow cytometry and by a spike-in method yielded similar results only on the phylum level. Even though the spike-in method identified the decrease of four genera, analysis by fluorescence-activated cell sorting (FACS) uncovered eight significantly reduced genera, such as Prevotella and Paraprevotella upon antibiotic treatment. …”

Table 9_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

“…In another experimental setting with tulathromycin treatment, bacterial abundance quantification by flow cytometry and by a spike-in method yielded similar results only on the phylum level. Even though the spike-in method identified the decrease of four genera, analysis by fluorescence-activated cell sorting (FACS) uncovered eight significantly reduced genera, such as Prevotella and Paraprevotella upon antibiotic treatment. …”

Image 3_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.tif by Stefanie Wagner (743707)

“…In another experimental setting with tulathromycin treatment, bacterial abundance quantification by flow cytometry and by a spike-in method yielded similar results only on the phylum level. Even though the spike-in method identified the decrease of four genera, analysis by fluorescence-activated cell sorting (FACS) uncovered eight significantly reduced genera, such as Prevotella and Paraprevotella upon antibiotic treatment. …”

Image 1_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.tif by Stefanie Wagner (743707)

“…In another experimental setting with tulathromycin treatment, bacterial abundance quantification by flow cytometry and by a spike-in method yielded similar results only on the phylum level. Even though the spike-in method identified the decrease of four genera, analysis by fluorescence-activated cell sorting (FACS) uncovered eight significantly reduced genera, such as Prevotella and Paraprevotella upon antibiotic treatment. …”

Image 2_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.tif by Stefanie Wagner (743707)

“…In another experimental setting with tulathromycin treatment, bacterial abundance quantification by flow cytometry and by a spike-in method yielded similar results only on the phylum level. Even though the spike-in method identified the decrease of four genera, analysis by fluorescence-activated cell sorting (FACS) uncovered eight significantly reduced genera, such as Prevotella and Paraprevotella upon antibiotic treatment. …”

Image 4_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.tif by Stefanie Wagner (743707)

“…In another experimental setting with tulathromycin treatment, bacterial abundance quantification by flow cytometry and by a spike-in method yielded similar results only on the phylum level. Even though the spike-in method identified the decrease of four genera, analysis by fluorescence-activated cell sorting (FACS) uncovered eight significantly reduced genera, such as Prevotella and Paraprevotella upon antibiotic treatment. …”

Transcriptional analysis of <i>L. infantum</i>-infected macrophages from female and male donors 6 hpi. by Annika Bea (17686341)

Transcriptional analysis of <i>L. infantum</i>-infected macrophages from female and male donors 16 hpi. by Annika Bea (17686341)

IFA reduced the capsular biosynthesis through altering bacterial metabolism. by Tingting Wang (123983)

Regression results of the Callaway method. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Regression results of crowding out effects. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Article data. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Overidentification test results. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Quantile regression results. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Instrumental variable regression results. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Other robust regression results. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Baseline regression results. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Results of propensity score matching. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Parallel trend test. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”

Placebo test. by Pu Miao (12886949)

“…During this lag period, the effect on the number of patent declines by 8.475% to 28.283%, while the impact on the number of citations of patents decreases by 55.696% to 73.214%. (4) The significant promotional effect of science and technology talent policies is most pronounced in non-state-owned enterprises and those with high R&D investment, but such policies do not have a notable impact on state-owned enterprises or those with low R&D investment. …”