Image 1_Swine influenza surveillance in Italy uncovers regional and farm-based genetic clustering.tiff ‌ L. Cavicchio (21751946)

“…</p>Material and methods<p>Passive surveillance, conducted from 2013 to 2022, involved 253 farms located in three regions, collecting over 3,000 samples that were tested for swIAV. …”

Image 8_Swine influenza surveillance in Italy uncovers regional and farm-based genetic clustering.tiff ‌ L. Cavicchio (21751946)

“…</p>Material and methods<p>Passive surveillance, conducted from 2013 to 2022, involved 253 farms located in three regions, collecting over 3,000 samples that were tested for swIAV. …”

Image 7_Swine influenza surveillance in Italy uncovers regional and farm-based genetic clustering.tiff ‌ L. Cavicchio (21751946)

“…</p>Material and methods<p>Passive surveillance, conducted from 2013 to 2022, involved 253 farms located in three regions, collecting over 3,000 samples that were tested for swIAV. …”

Image 4_Swine influenza surveillance in Italy uncovers regional and farm-based genetic clustering.tiff ‌ L. Cavicchio (21751946)

“…</p>Material and methods<p>Passive surveillance, conducted from 2013 to 2022, involved 253 farms located in three regions, collecting over 3,000 samples that were tested for swIAV. …”

Image 2_Swine influenza surveillance in Italy uncovers regional and farm-based genetic clustering.tiff ‌ L. Cavicchio (21751946)

“…</p>Material and methods<p>Passive surveillance, conducted from 2013 to 2022, involved 253 farms located in three regions, collecting over 3,000 samples that were tested for swIAV. …”

Image 9_Swine influenza surveillance in Italy uncovers regional and farm-based genetic clustering.tiff ‌ L. Cavicchio (21751946)

“…</p>Material and methods<p>Passive surveillance, conducted from 2013 to 2022, involved 253 farms located in three regions, collecting over 3,000 samples that were tested for swIAV. …”

Image 5_Swine influenza surveillance in Italy uncovers regional and farm-based genetic clustering.tiff ‌ L. Cavicchio (21751946)

“…</p>Material and methods<p>Passive surveillance, conducted from 2013 to 2022, involved 253 farms located in three regions, collecting over 3,000 samples that were tested for swIAV. …”

Image 3_Swine influenza surveillance in Italy uncovers regional and farm-based genetic clustering.tiff ‌ L. Cavicchio (21751946)

“…</p>Material and methods<p>Passive surveillance, conducted from 2013 to 2022, involved 253 farms located in three regions, collecting over 3,000 samples that were tested for swIAV. …”

Image 6_Swine influenza surveillance in Italy uncovers regional and farm-based genetic clustering.tiff ‌ L. Cavicchio (21751946)

“…</p>Material and methods<p>Passive surveillance, conducted from 2013 to 2022, involved 253 farms located in three regions, collecting over 3,000 samples that were tested for swIAV. …”

Table 1_Under pressure: effect of first-parity sows’ early social experience and genetic line on behavior and lesions in a paired interaction test with an older sow.docx ‌ Linda Marie Backeman Hannius (22818311)

Table 1_Public attitudes, perceptions, and suggested strategies for managing free-roaming dogs in selected urban and rural settings in Uganda.docx ‌ Dickson Stuart Tayebwa (6733610)

Table 2_Public attitudes, perceptions, and suggested strategies for managing free-roaming dogs in selected urban and rural settings in Uganda.docx ‌ Dickson Stuart Tayebwa (6733610)

Data Sheet 1_Swine influenza surveillance in Italy uncovers regional and farm-based genetic clustering.pdf ‌ L. Cavicchio (21751946)

“…</p>Material and methods<p>Passive surveillance, conducted from 2013 to 2022, involved 253 farms located in three regions, collecting over 3,000 samples that were tested for swIAV. …”

Data Sheet 1_Biosecurity implementation in poultry farms across Europe and neighboring countries: a systematic review.zip ‌ Ronald Vougat Ngom (18239415)

“…Despite relatively broad geographical coverage, including eight multi-country studies involving 36 national assessments, the distribution of studies was uneven. …”

Table 6_AI-based predictive modeling for enteric methane mitigation: cross-farm validation using an allicin based essential oil.docx ‌ Yaniv Altshuler (645084)

“…Since the wide variety of feed additives available in the market, validating the model across a diverse range of additives is critical for its adoption in commercial farming practices. In this study, we extensively validate the model across ten commercial farms over a three-month period, involving 339 Holstein cows, and using an allicin-based essential oil (Allimax), an organosulfur compound obtained from garlic with potential to reduce enteric methane emissions. …”

Table 4_AI-based predictive modeling for enteric methane mitigation: cross-farm validation using an allicin based essential oil.docx ‌ Yaniv Altshuler (645084)

“…Since the wide variety of feed additives available in the market, validating the model across a diverse range of additives is critical for its adoption in commercial farming practices. In this study, we extensively validate the model across ten commercial farms over a three-month period, involving 339 Holstein cows, and using an allicin-based essential oil (Allimax), an organosulfur compound obtained from garlic with potential to reduce enteric methane emissions. …”

Table 5_AI-based predictive modeling for enteric methane mitigation: cross-farm validation using an allicin based essential oil.docx ‌ Yaniv Altshuler (645084)

“…Since the wide variety of feed additives available in the market, validating the model across a diverse range of additives is critical for its adoption in commercial farming practices. In this study, we extensively validate the model across ten commercial farms over a three-month period, involving 339 Holstein cows, and using an allicin-based essential oil (Allimax), an organosulfur compound obtained from garlic with potential to reduce enteric methane emissions. …”

Table 1_AI-based predictive modeling for enteric methane mitigation: cross-farm validation using an allicin based essential oil.docx ‌ Yaniv Altshuler (645084)

“…Since the wide variety of feed additives available in the market, validating the model across a diverse range of additives is critical for its adoption in commercial farming practices. In this study, we extensively validate the model across ten commercial farms over a three-month period, involving 339 Holstein cows, and using an allicin-based essential oil (Allimax), an organosulfur compound obtained from garlic with potential to reduce enteric methane emissions. …”

Table 2_AI-based predictive modeling for enteric methane mitigation: cross-farm validation using an allicin based essential oil.docx ‌ Yaniv Altshuler (645084)

“…Since the wide variety of feed additives available in the market, validating the model across a diverse range of additives is critical for its adoption in commercial farming practices. In this study, we extensively validate the model across ten commercial farms over a three-month period, involving 339 Holstein cows, and using an allicin-based essential oil (Allimax), an organosulfur compound obtained from garlic with potential to reduce enteric methane emissions. …”

Table 3_AI-based predictive modeling for enteric methane mitigation: cross-farm validation using an allicin based essential oil.docx ‌ Yaniv Altshuler (645084)

“…Since the wide variety of feed additives available in the market, validating the model across a diverse range of additives is critical for its adoption in commercial farming practices. In this study, we extensively validate the model across ten commercial farms over a three-month period, involving 339 Holstein cows, and using an allicin-based essential oil (Allimax), an organosulfur compound obtained from garlic with potential to reduce enteric methane emissions. …”