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This article is prepared to help Extension agents, farmers, farm managers, researchers, graduate students, and the general public better understand AI, associated terms, and technologies. Written by Young Gu Her, Nikolay Bliznyuk, Yiannis Ampatzidis, Ziwen Yu, and Haimanote Bayabil, and published by the UF/IFAS Department of Agricultural and Biological Engineering, September 2024.

Abstract Extracellular vesicles (EVs) are vesicular structures comprised of a bilayer lipid membrane, released by living cells. There is a growing body of evidence for their functionality, indicating that small EVs (sEVs) can mediate specific forms of intercellular communication. The future applications of sEVs hold great promise, not only as diagnostic markers but also as therapeutic agents. However, the greatest difficulty in the clinical translation of sEVs is that they are currently poorly understood, especially concerning their in vivo behaviour. In this study, we provide a novel method for monitoring sEVs in blood circulation based on in vivo flow cytometry (IVFC). We have demonstrated that the height of the IVFC signal baseline is proportional to the concentration of sEVs. Moreover, we have found out that the peaks in the IVFC signal are generated by the aggregation or cellular uptake of sEVs. In vivo monitoring of sEVs clearance from the blood indicates that PEGylated sEVs have a longer residence time and exhibit less aggregation in circulation compared to non‐PEGylated sEVs. These studies reveal that IVFC enables real‐time in vivo monitoring of circulating sEVs, which can provide valuable insights into the pharmacokinetics and cellular targeting capabilities of sEVs.

This publication is part of a larger body of work initiated by the UF/IFAS Extension Climate and Carbon Extension Educators that informs a general audience about climate issues and mitigation strategies. It was prepared to help Extension agents, farmers, and ranchers interested in carbon credit programs better understand the process of carbon credit trading, from credit generation to markets. Note that because carbon markets are relatively new, programs and prices are likely to change over time. This publication includes a list of resources for further information.

In invasive lung adenocarcinoma (LUAD), patients with micropapillary (MIP) or solid (SOL) components had a significantly poorer prognosis than those with only lepidic (LEP), acinar (ACI) or papillary (PAP) components. It is interesting to explore the genetic features of different histologic subtypes, especially the highly aggressive components.Based on a cohort of 5,933 patients, this study observed that in different tumor size groups, LUAD with MIP/SOL components showed a different prevalence, and patients with ALK alteration or TP53 mutations had a higher probability of developing MIP/SOL components. To control individual differences, this research used spatial whole-exome sequencing (WES) via laser-capture microdissection of five patients harboring these five coexistent components and identified genetic features among different histologic components of the same tumor. In tracing the evolution of components, we found that titin (TTN) mutation might serve as a crucial intratumor potential driver for MIP/SOL components, which was validated by a cohort of 146 LUAD patients undergoing bulk WES. Functional analysis revealed that TTN mutations enriched the complement and coagulation cascades, which correlated with the pathway of cell adhesion, migration, and proliferation.Collectively, the histologic subtypes of invasive LUAD were genetically different, and certain trunk genotypes might synergize with branching TTN mutation to develop highly aggressive components.

Abstract To tackle misdiagnosis in lung cancer screening with low‐dose computed tomography (LDCT), we aimed to compile a genome atlas for differentiating benign, preinvasive, and invasive lung nodules and characterize their molecular pathogenesis. We collected 432 lung nodule tissue samples from Chinese patients, spanning benign, atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA). We performed comprehensive sequencing, examining somatic variants, gene expressions, and methylation levels. Our findings uncovered EGFR and TP53 mutations as key drivers in ‐ early lung cancer development, with EGFR mutation frequency increasing with disease progression. Both EGFR mutations and EGF/EGFR hypo‐methylation activated the EGFR pathway, fueling cancer growth. Transcriptome analysis identified four lung nodule subtypes (G1‐4) with distinct molecular features and immune cell infiltrations: EGFR‐driven G1, EGFR/TP53 co‐mutation G2, inflamed G3, stem‐like G4. Estrogen/androgen response was associated with the EGFR pathway, proposing a new therapy combining tyrosine kinase inhibitors with antiestrogens. Preinvasive nodules exhibited stem cell pathway enrichment, potentially hindering invasion. Epigenetic regulation of various genes was essential for lung cancer initiation and development. This study provides insights into the molecular mechanism of neoplastic progression and identifies potential diagnostic biomarkers and therapeutic targets for lung cancer.

This publication discusses information about the structure of the carbon market in the United States and some restrictions on participation. It also provides an overview of the benefits and barriers of farmers’ or landowners’ participation in the carbon market, especially for Florida. Written by Ziwen Yu, and published by the UF/IFAS Department of Agricultural and Biological Engineering, February 2024.

In this study, a novel series of histone deacetylases 6 (HDAC6) inhibitors containing polycyclic aromatic rings were discovered and evaluated for their pharmacological activities. The most potent compound 10c exhibited high HDAC6 inhibitory activity (IC50 = 261 nM) and excellent HDAC6 selectivity (SI = 109 for HDAC6 over HDAC3). 10c also showed decent antiproliferative activity in vitro with IC50 of 7.37–21.84 μM against four cancer cell lines, comparable to that of tubastatin A (average IC50 = 6.10 μM). Further mechanism studies revealed that 10c efficiently induced apoptosis and S-phase arrest in B16-F10 cells. In addition, 10c markedly increased the expression of acetylated-α-tubulin both in vitro and in vivo, without affecting the levels of acetylated-H3 (marker of HDAC1 inhibition). Furthermore, 10c (80 mg/kg) exhibited moderate antitumor efficacy in a melanoma tumour model with a tumour growth inhibition (TGI) of 32.9%, comparable to that (TGI = 31.3%) of tubastatin A. Importantly, the combination of 10c with NP19 (a small molecule PD-L1 inhibitor discovered by us before) decreased tumour burden substantially (TGI% = 60.1%) as compared to monotherapy groups. Moreover, the combination of 10c with NP19 enhanced the anti-tumour immune response, mediated by a decrease of PD-L1 expression levels and increased infiltration of anti-tumour CD8+ T cells in tumour tissues. Collectively, 10c represents a novel HDAC6 inhibitor deserving further investigation as a potential anti-cancer agent.

Abstract To reveal the correlation of dynamic serum tumor markers (STMs) and molecular features of epidermal growth factor receptor‐mutated (EGFR‐mutated) lung cancer during targeted therapy, we retrospectively reviewed 303 lung cancer patients who underwent dynamic STM tests [neuron‐specific enolase (NSE), carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125), carbohydrate antigen 153 (CA153), the soluble fragment of cytokeratin 19 (CYFRA21‐1), and squamous cell carcinoma antigen (SCC)] and circulating tumor DNA (ctDNA) testing with a panel covering 168 genes. At baseline, patients with EGFR mutation trended to have abnormal CEA, abnormal CA153, and normal SCC levels. Additionally, patients with Thr790Met (T790M) mutation were more likely to have abnormal CEA levels than patients without T790M mutation. Among patients with secondary resistance to EGFR tyrosine kinase inhibitors (TKI), the dynamic STMs showed a descending trend in the responsive stage and a rising trend in the resistant stage. However, the changing slopes differed between T790M subgroup and the non‐T790M subgroup in individual STMs. Our study demonstrated that the combination of baseline levels and variations of STMs (including the responsive stage and resistant stage) can be suggestive of secondary EGFR‐T790M mutation [area under the curve (AUC) = 0.897] and that changing trends of STMs (within 8 weeks after initiating the TKI therapy) can be potential predictors for the clearance of EGFR ctDNA [AUC = 0.871]. In conclusion, dynamic monitoring STMs can help to predict the molecular features of EGFR‐mutated lung cancer during targeted therapy.

This publication aims to introduce readers to recent crop yield forecasting approaches based on artificial intelligence (AI) and to provide examples of how AI can potentially improve yield forecasting at the field and regional levels. Written by Clyde Fraisse, Yiannis Ampatzidis, Sandra Guzmán, Wonsuk Lee, Christopher Martinez, Sanjay Shukla, Aditya Singh, and Ziwen Yu, and published by the UF/IFAS Department of Agricultural and Biological Engineering, April 2022.

Background: The responses of cancer patients to immune checkpoint inhibitors (ICIs) vary in success. CD8+ tumor infiltrating lymphocytes (TILs) play a key role in killing tumor cells. This study aims to evaluate the prognostic role of CD8+ TILs in cancer patients treated with ICIs. Methods: We systematically searched all publications from PubMed, EMBASE, and Cochrane Library until 12 Jul 2021 without any restriction of language or article types. Studies assessing high versus low CD8+ TILs in predicting efficacy and survival of various cancer patients were included. The outcomes included overall survival (OS), progression-free survival (PFS), and objective response rate (ORR). The study protocol is prospectively registered on PROSPERO (registration number CRD42021233654). Findings: Findings: A total of 33 studies consisting of 2559 cancer patients were included. The result showed that high CD8+ TILs were significantly associated with better OS (HR, 0.52; 95% confidence interval: 0.41–0.67; p

This publication aims to clarify the concerns regarding data ownership and explain the responsibilities of that ownership, sharing, and benefits in a collaborative smart farming application. Written by Ziwen Yu, Albert De Vries, Yiannis Ampatzidis, and D. Daniel Sokol, and published by the UF/IFAS Department of Agricultural and Biological Engineering, October 2021.

This publication introduces the TIGER/Line Shapefile datasets and demonstrates how we can extract and map spatial information from the US Census data using the shapefiles. Written by Young Gu Her and Ziwen Yu, and published by the UF/IFAS Department of Agricultural and Biological Engineering, May 2021.

This publication discusses heat-related deaths in Florida from 2010 to 2020 and presents safety recommendations as well as useful resources to prevent heat-related illnesses. Written by Serap Gorucu, Clyde Fraisse, and Ziwen Yu, and published by the UF/IFAS Department of Agricultural and Biological Engineering, May 2021.

Internet of Things (IoT) has been widely used in agriculture for smart farming and precision agriculture to improve efficiency and effectiveness in a data-oriented fashion. Monitored data is collected at a much higher frequency and in relatively higher reliability and accuracy than those of the manually collected information. However, there is a challenge with such large data sets in how the data is stored and processed to keep the information completeness and meet the dynamic requirements for future development and usage. The data from such systems typically consists readings from monitoring sensors and corresponding attributes, such as location, units, measurement types (e.g. air temperature, wind speed, air pressure) for each measurement. Problems such as huge data redundancy, loss of information, and inflexible data structure may occur and cost substantial time and investment for IoT system managers who do not have the necessary professional training and are not using any IT consulting service. This publication discusses the typical attributes in IoT monitoring networks and presents a data structure that outlines best practices in organizing data. The main goal is to provide knowledge on how such a system’s data should be structured so that these IoT monitoring network owners, such as agricultural producers, can optimize how they use their data. Thus, the primary audience is agricultural producers, consultants and others who install, manage, or use such IoT monitoring systems for decision making.

Precipitation is one of the many important natural factors impacting agriculture and natural resource management. Although statistics have been applied to investigate the non-stationary trend and the unpredictable variances of precipitation under climate change, existing methods usually lack a sound physical basis that can be generally applied in any location and at any time for future extrapolation, especially in tropical areas. Physically, the formation of precipitation is a result of ascending air which reduces air pressure and condenses moisture into drops, either by irregular terrain or atmospheric phenomena (e.g., via frontal lifting). Thus, in this paper, pressure change events (PCEs) will be used as a physical indicator of the stability of atmospheric systems to reveal the impact of temperature on precipitation in the tropical areas of Florida. By using data from both national and regional weather observation networks, this study segments the continuous observation series into PCE sequences for further analysis divided by dry and wet seasons. The results reveal that the frequency and intensity of PCE are highly associated with the occurrences of weather events. Decreasing pressure favors precipitation, and may turn extreme when the temperature and air moisture are sufficient to fuel the process. With similar intensity, decreasing pressure change events (DePCEs) generally bear a higher probability of precipitation (POP) and precipitation depth (PD) than increasing pressure change events (InPCEs). The frequency of alternating between InPCEs and DePCEs is subject to the temperature of the season and climate. Due to the seasonal fluctuations of weather characteristics, such as temperature and relative humidity, the dependence of extreme precipitation on these characteristics can be interpreted via PCE. A 7% increase rate of precipitation vs. temperature rise, determined by the Clausius—Clapeyron (C—C) relationship, can be observed from extreme precipitation with variances in the season and PCE types. Although indicated by other research, active vertical movement of air caused by a phase change in water at the frozen point is not pronounced in Florida. The response patterns of humidity to precipitation also vary by season and PCE types in extreme conditions. In summary, PCEs demonstrate reliable physical evidence of precipitation formation and can better associate the occurrence and intensity of extreme weather with other characteristics. In turn, such associations embody the underlying physical concepts present at any location in the world.

Driving characteristics of lane departure can provide design principles for lane departure warning system and lane keeping assist system. In this article, based on driving simulator experiments of unintentional lane departure, the operation performances of human drivers, the motion characteristics of vehicles, and the relative motion between the vehicle and lane line are synthetically studied. First, unintentional lane departure is classified into lane departure by fatigue and lane departure by secondary task. Subsequently, two simulator experiments of fatigue-based lane departure and secondary task–based are designed and performed to collect the synchronous driver–vehicle–road data. The data of steering angle, steering angle velocity, steering angle entropy, lateral acceleration, lateral velocity, and yaw velocity, under fatigue-based and secondary task–based lane departure are collected and compared with those gathered under normal lane changing. Results show that the characteristics of unintentional lane departures differ from that of normal lane departure changing. Furthermore, the characteristics of fatigue-based lane departures are shown some differences with that of secondary task–based ones.

Abstract Background Leveraging Alzheimer’s disease (AD) imaging biomarkers and longitudinal cognitive data may allow us to establish evidence of cognitive resilience (CR) to AD pathology in-vivo. Here, we applied latent class mixture modeling, adjusting for sex, baseline age, and neuroimaging biomarkers of amyloid, tau and neurodegeneration, to a sample of cognitively unimpaired older adults to identify longitudinal trajectories of CR. Methods We identified 200 Harvard Aging Brain Study (HABS) participants (mean age = 71.89 years, SD = 9.41 years, 59% women) who were cognitively unimpaired at baseline with 2 or more timepoints of cognitive assessment following a single amyloid-PET, tau-PET and structural MRI. We examined latent class mixture models with longitudinal cognition as the dependent variable and time from baseline, baseline age, sex, neocortical Aβ, entorhinal tau, and adjusted hippocampal volume as independent variables. We then examined group differences in CR-related factors across the identified subgroups from a favored model. Finally, we applied our favored model to a dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI; n = 160, mean age = 73.9 years, SD = 7.6 years, 60% women). Results The favored model identified 3 latent subgroups, which we labelled as Normal (71% of HABS sample), Resilient (22.5%) and Declining (6.5%) subgroups. The Resilient subgroup exhibited higher baseline cognitive performance and a stable cognitive slope. They were differentiated from other groups by higher levels of verbal intelligence and past cognitive activity. In ADNI, this model identified a larger Normal subgroup (88.1%), a smaller Resilient subgroup (6.3%) and a Declining group (5.6%) with a lower cognitive baseline. Conclusion These findings demonstrate the value of data-driven approaches to identify longitudinal CR groups in preclinical AD. With such an approach, we identified a CR subgroup who reflected expected characteristics based on previous literature, higher levels of verbal intelligence and past cognitive activity.

IntroductionThe objective of this study was to examine the impact of Codonopsis pilosula and Astragalus membranaceus extract (CA) on the growth performance, diarrhea rate, immune function, antioxidant capacity, gut microbiota, and short-chain fatty acids (SCFAs) in weaned piglets.MethodsA total of forty-eight 31-day-old weaned piglets, were divided into four groups randomly based on the treatment type: control group (CON), low dose group (LCA, 0.5% CA), medium dose group (MCA, 1.0% CA), and high dose group (HCA, 1.5% CA), and were fed for a duration of 28 days. On the morning of the 1st and 29th day, the piglets were assessed by weighing them on an empty stomach, recording their daily feed intake and diarrhea rate.ResultsCA increased the average daily weight gain and reduced F/G without significant differences, and the diarrhea rate was reduced in the LCA and MCA groups. Furthermore, the levels of T-AOC, SOD, GSH-Px, and MDA were increased. The levels of T-AOC in the LCA group and the MCA group, SOD in the MCA group, and GSH-Px in the HCA group were significantly higher compared with the CON group (p < 0.05). Additionally, CA significantly increased IgM, IgG, and IgA levels (p < 0.05). The results of gut microbiota analysis showed that the bacterial population and diversity of faeces were changed with the addition of CA to basal diets. CA increased the abundance of the beneficial bacterial Firmicutes and Lactobacillus. Additionally, Compared with the CON group, CA significantly increased the SCFAs content of weaned piglets (p < 0.05).DiscussionCA can alleviate oxidative stress, improve immunity and antioxidant capacity, increase the abundance of beneficial bacteria, and the content of SCFAs for improving the intestinal barrier of piglets, thus promoting growth and reducing diarrhea rate in weaned piglets.

Abstract Background Salmonella enterica subspecies enterica serovar abortus equi (S. abortus equi) is one of the main pathogens that causes abortion in pregnant horses and donkeys, which was highly infectious and greatly restricts the healthy development of the horse industry. Objectives In order to investigate the prevalence and biological characteristics of S. abortus equi in different regions and breeds of horses in Xinjiang. Methods This study conducted ELISA detection of S. abortus equi antibodies on serum samples of 971 horses collected from three large-scale horse farms and five free-range horse farms in Yili Prefecture and Bayingol Mongolian Autonomous Prefecture of Xinjiang from 2020 to 2023. On this basis, bacterial isolation, culture, identification, and drug sensitivity tests were conducted on 42 samples of aborted foal tissues and 23 mare vaginal swabs. Results The results showed that the positive rate of S. abortus equi antibody was as high as 20.91% in 971 horse serum samples. Among them, the positive rate in the Ili region (29.09%) was significantly higher than that in the Bayingole region (11.24%), and the positive rate in mares (22.45%) was higher than that in stallions (14.05%). In terms of horse breeds, the positive rates of self-propagating thoroughbred horses, half-bred horses, Ili horses and Yanqi horses were 43.22%, 28.81%, 14.72% and 11.24% respectively. In addition, S. abortus equi was more susceptible to juvenile and elderly horses, with positive rates of 70.00%and 41.86%, respectively, both of which were significantly higher than young (10.97%) and adult (19.79%) horses. Further, 9 strains of S. abortus equi were obtained through bacterial isolation, culture and identification, which were resistant to five antibiotics (Clarithromycin, Clindamycin, penicillin, Sulfamethoxazole and Rifampicin), and sensitive to 13 antimicrobial agents (Amoxicillin, Ciprofloxacin and Gentamicin, et al.). Conclusion There was a high infection rate of S. abortus equi in Ili Prefecture and self-propagating thoroughbred horses, and juvenile or old mares were more susceptible, which will provide scientific basis for the prevention of S. abortus equi infection in different regions and breeds of horses in Xinjiang.

ObjectivesPost-stroke depression (PSD) may be associated with the altered brain network property. This study aimed at exploring the brain network characteristics of PSD under the classic cognitive task, i.e., the oddball task, in order to promote our understanding of the pathogenesis and the diagnosis of PSD.MethodsNineteen stroke survivors with PSD and 18 stroke survivors with no PSD (non-PSD) were recruited. The functional near-infrared spectroscopy (fNIRS) covering the dorsolateral prefrontal cortex was recorded during the oddball task state and the resting state. The brain network characteristics were extracted using the graph theory and compared between the PSD and the non-PSD subjects. In addition, the classification performance between the PSD and non-PSD subjects was evaluated using features in the resting and the task state, respectively.ResultsCompared with the resting state, more brain network characteristics in the task state showed significant differences between the PSD and non-PSD groups, resulting in better classification performance. In the task state, the assortativity, clustering coefficient, characteristic path length, and local efficiency of the PSD subjects was larger compared with the non-PSD subjects while the global efficiency of the PSD subjects was smaller than that of the non-PSD subjects.ConclusionThe altered brain network properties associated with PSD in the cognitive task state were more distinct compared with the resting state, and the ability of the brain network to resist attack and transmit information was reduced in PSD patients in the task state.SignificanceThis study demonstrated the feasibility and superiority of investigating brain network properties in the task state for the exploration of the pathogenesis and new diagnosis methods for PSD.