<i>Oenocarpus bacaba</i> palm tree (A) and fruit (B). by Eudes Alves Simões-Neto (19697968)

“…Serological cure was achieved in 34.6% of cases, and IgG titers decreased in 15.3%.</p><p>Conclusions and significance</p><p>We encountered several barriers in managing ACD, including population vulnerability, reliance on outdated diagnostic techniques, lack of standardized molecular biology methods, and limited therapeutic options. …”

NIE ELISA results. by Jannet A. Tobon Ramos (20855642)

“…Twelve months post-MDA, hookworm prevalence decreased to 56.5% in DA- and 34.4% in IDA-treated villages, respectively (<i>p</i><0.001, both comparisons). …”

Kato Katz and PCR database 12 months collection. by Jannet A. Tobon Ramos (20855642)

“…Twelve months post-MDA, hookworm prevalence decreased to 56.5% in DA- and 34.4% in IDA-treated villages, respectively (<i>p</i><0.001, both comparisons). …”

Master database baseline, 4 weeks and 1 year. by Jannet A. Tobon Ramos (20855642)

“…Twelve months post-MDA, hookworm prevalence decreased to 56.5% in DA- and 34.4% in IDA-treated villages, respectively (<i>p</i><0.001, both comparisons). …”

Final dataset. by Keiko Ochiai (21453423)

“…While a significant association between these attributes was observed, a comprehensive path analysis revealed that each attribute independently correlated with higher SDQ scores, suggesting that child maltreatment leads to these difficulties through accelerated aging and decreased eye contact. …”

Demographic Characteristics of Participants. by Keiko Ochiai (21453423)

“…While a significant association between these attributes was observed, a comprehensive path analysis revealed that each attribute independently correlated with higher SDQ scores, suggesting that child maltreatment leads to these difficulties through accelerated aging and decreased eye contact. …”

Illustrative diagram of the three crash types. by Chun-Chieh Chao (2681722)

“…</p><p>Results</p><p>Significant risk factors for overtaking crashes included heavy goods vehicles (HGVs) as crash partners (AOR = 1.30, 95% CI 1.27–1.33), and elderly crash partners (AOR = 2.01, 95% CI = 1.94–2.09), and decreased risk in rural area with speed limits of 20–30 miles per hour (AOR = 0.45, 95% CI = 0.43–0.47). …”

Multivariate logistic regression results. by Chun-Chieh Chao (2681722)

“…</p><p>Results</p><p>Significant risk factors for overtaking crashes included heavy goods vehicles (HGVs) as crash partners (AOR = 1.30, 95% CI 1.27–1.33), and elderly crash partners (AOR = 2.01, 95% CI = 1.94–2.09), and decreased risk in rural area with speed limits of 20–30 miles per hour (AOR = 0.45, 95% CI = 0.43–0.47). …”

Univariate logistic regression results. by Chun-Chieh Chao (2681722)

“…</p><p>Results</p><p>Significant risk factors for overtaking crashes included heavy goods vehicles (HGVs) as crash partners (AOR = 1.30, 95% CI 1.27–1.33), and elderly crash partners (AOR = 2.01, 95% CI = 1.94–2.09), and decreased risk in rural area with speed limits of 20–30 miles per hour (AOR = 0.45, 95% CI = 0.43–0.47). …”

Some examples of selected Chinese characters. by Weijia Zhu (65481)

“…To address these challenges, we propose esFont, a novel guided Diffusion framework. …”

Primers for RT-qPCR. by Jiaoyang Li (2862269)

“…To elucidate the function of NSP2 during PRRSV infection, we identified SH3KBP1 as an NSP2-interacting host protein using mass spectrometry. …”

Oligonucleotide primers for gene expression. by Cian Reid (17274007)

“…This hyper-induction of IL-6 was observed most significantly in response to TLR1/2 stimulation in TUS positive calves. …”

Calf health information. by Cian Reid (17274007)

“…This hyper-induction of IL-6 was observed most significantly in response to TLR1/2 stimulation in TUS positive calves. …”

Mean parameter values for the selected crops. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Performance comparison of ML models. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Comparative data of different soil samples. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Confusion matrix of random forest model. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Sensor value scenario for fuzzy logic algorithm. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Evaluation metrics of selected ML models. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Block diagram of the proposed system. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”