The study of post-mortem changes is a crucial component of forensic investigation. Human forensic taphonomic facilities (HFTFs) are the only institutions allowing the design and execution of controlled human decomposition experiments. When bodies are skeletonized, bones are normally stored in skeletal collections and used for anthropological studies. However, HFTFs apply chemical and/or thermal treatments to the remains prior bone long-term storage. These treatments are believed to alter heavily the original biochemical and molecular signature of bone material. The present study aims to evaluate the effect of these procedures on the bone metabolome and lipidome by using an animal bone model. Three intact bovine tibiae were processed using three protocols routinely applied at HFTFs, and their three counterparts were used as non-treated controls. Bone powder samples were subjected to biphasic extraction and both metabolites and lipids were analysed via liquid chromatography tandem mass-spectrometry. Results showed severe reductions in the abundances of both metabolites and lipids, and the presence of contamination introduced by cleaning agents. Despite the preliminary nature of the study, we demonstrated that the biochemical profile of bone is heavily affected by the maceration procedures. Ideally, these treatments should be avoided, or replaced by minimally invasive procedures agreed across HFTFs. Human forensic taphonomic facilities (HFTFs) are becoming increasingly popular as they allow the comprehensive study of the complex interplay of the intrinsic and extrinsic factors that contribute to cadaveric decomposition. To date, there are eleven HFTFs worldwide including eight in the USA 8), Australia 1), Canada (1) and Europe (1), each operating differently depending on the national or state laws regarding treatment and disposition of human remains. The human skeletal remains associated with HFTFs can provide a valuable resource for conducting biomolecular studies that have significant potential for forensic applications such as PMI estimation , Canada ,3,4,5,6., AustralAll the above-mentioned studies clearly show the potential of metabolomics in forensic science, as well as and the importance of HFTFs in providing unvaluable human models for the study of decomposition. However, while there are some commonalities amongst all the facilities, many of the procedures such as chemical and thermal processing skeletal remains to remove soft tissues and fluids are unique to each facility. The lack of common guidelines between HFTFs on the treatment of bones for long-term storage (a procedure known as “soft maceration”) significantly impacts the possibility of conducting biomolecular research on the donated osteological collections across different HFTFs, thereby drastically limiting the potential of obtaining comparable forensic and taphonomic information from the bones when conducting novel research involving state-of-the-art biochemical and molecular methods. As an example, the National Institute of Standards and Technology (NIST) guidelines clearly advise against the use of excessive soaking and the use of heat and chemical agents on human bones  as this These facilities have now been in use from several decades and keep offering invaluable samples for anthropological research. However, there is a lack of standardized guidelines for the treatment of human remains. For the first time, to the best of our knowledge, we are highlighting an issue that could compromise all future metabolomics studies based on such material. The current study aims to investigate the effect of three different maceration procedures developed on routinely performed protocols at Forensic Anthropology Center at Texas State University (FACTS) facility (n = 1), employing a bath of deionized water in laundry detergent at 87 °C, and from the Southeast Texas Applied Forensic Science (STAFS) facility (n = 2), consisting of a milder bath of detergent and deionized water at 55 °C, on metabolites and lipids extracted from bovine bone tissue and analyzed by LC-MS/MS. Results of the present study have the objective of clarifying whether these protocols are suitable or not to preserve the biomolecular integrity of the bone matrix. In particular, we aim at providing a preliminary understanding on how different protocols could differentially affect the bone metabolomic and lipidomic profile in order to ensure the comparability of studies conducted on material sourced at different HFTFs, to increase the awareness on this issue and to eventually stimulate debates on future change in policies for more appropriate and standardized treatment of human remains.Three pairs of bovine tibiae were purchased from a local butcher and used in the present study. These bovid samples are animal by-products under the guidelines and licensing of the Department for Environment, Food and Rural Affairs (DEFRA). Experimental procedures were conducted within the premise of the DEFRA license. The left bones were sampled in their native state  while each of the right bones were subjected to a different maceration protocol: (1) a solution of Dish soap liquid wash:deionized water (1:2% v/v) for one week at 55 °C; (2) Dish soap liquid wash:deionized water (1:2% v/v) for two days at 55 °C; (3) Laundry detergent:deionized water (2 oz/15 L) for two days at 87 °C . The thr®® ACS was purchased from VWR Chemicals . Water Optima™ LC/MS Grade, Methanol (MeOH) Optima™ LC/MS Grade, Pierce™ Acetonitrile (ACN), LC-MS Grade and Isopropanol (IPA), Optima™ LC/MS Grade were purchased from Thermo Scientific . In total two replicates for each of the six specimens were collected and extracted according to a modified Folch et al. [Chloroform (Chl), AnalaR NORMAPURh et al.  protocol4 with an injection time of 50 ms. An extraction blank was used to create background exclusion list and a pooled quality control (QC) were used to create the inclusion list. Hydrophilic Liquid Interaction Chromatography (HILIC) separation was performed on a Waters Acquity UPLC BEH amide column (2.1 × 150 mm with particle size of 1.7 μm) operating at 45 °C with a flow rate of 200 μL/min. The liquid chromatography (LC) gradient consisted of a binary buffer system, buffer A (LC/MS grade water) and buffer B (LC/MS grade ACN), both containing 10 mM ammonium formate. Independent buffers systems were used for positive and negative ionization mode acquisition, respectively; for positive ionization mode buffers pH was adjusted using 0.1% formic acid, whereas for negative ionization mode buffers pH was adjusted using 0.1% v/v ammonia solution. The LC gradient was the same for both polarities, namely 95% B at T0 hold for 1.5 min followed by a linear decrease to 50% B in 11 min, hold for 4 min, return to the starting conditions, and hold for further 4.5 min (column stabilization). The voltage applied for positive and negative ionization modes was 3.5 kV and 2.5 kV, respectively. Injection volumes used were 5 μL for positive ionization mode (ESI+) and 10 μL for negative ionization mode (ESI−).The metabolomic characterization of the bone samples was performed on a Thermo Scientific  Vanquish Liquid chromatography system connected to an ID-X High Resolution Mass Spectrometer (MS) system. The MS data were acquired using the AcquireX acquisition workflow . The MS parameters were set as follows: MS1 mass resolution 60 K, for MS2 30 K stepped energy (HCD) 20, 25, 50 scan range 100–1000, RF len (%) 35, AGC gain, intensity was set to 25% and minimum intensity threshold was set to 24 25% custom injection mode with an injection time of 50 ms. The chromatographic separation was performed on a Waters Acquity UPLC CSH C18 column (2.1 × 150 mm with particle size of 1.7 μm part no. 186005298), operating at 55 °C with flow rate of 200 μL/min. The LC gradient consists of a binary buffer system, buffer A (LC/MS grade water/ACN 40/60% v/v) and buffer B (90:10% v/v IPA and ACN) both containing 10 mM ammonium formate. Independent buffers systems were used for positive and negative ionization acquisition modes; for ESI+ buffers pH was adjusted using 0.1% formic acid, whereas for ESI− buffers pH was adjusted using 0.1% ammonia solution. The LC gradients were the same for both polarities: 60% B at T0 hold for 1.5 min followed by a linear increase to 85% B at 7 min, a further increase to 95% B at 12.5 min and hold for 4.5 min, return to starting condition and hold for further 4.5 min (column stabilization). Injection volumes used were 3 μL ESI+ and 5 μL for ESI-. The voltage applied for ESI+ and ESI- was 3.5 kV and 2.5 kV, respectively. The heated Electrospray Ionization (ESI) conditions for 200 μL were as follows: Sheath Gas: 35, Aux Gas 7 and Sweep Gas of 0. Ion Transfer tube Temp was: 300 °C and Vaporizer Temp was 275 °C. The ESI Conditions were the same for both metabolomics and lipidomics.The MS data were acquired using the AcquieX acquisition workflow . The MS operating parameters were as follows: MS1 mass resolution 60 K, MS2 30 K scan range 250–1250, stepped energy (HCD) 20, 25, 50. RF len (%) 35, AGC gain, intensity threshold 2The metabolomic ESI+ and ESI− data sets were processed via Compound Discoverer 3.2 using the following settings: untargeted metabolomic workflow, parent ion mass tolerance 10 ppm, alignment model adaptive curve, minimum intensity 16 signal to noise ratio (S/N) threshold 0.3 min, compound consolidation, retention time tolerance 0.3 min. Database matching were performed at MS2 level using Thermo Scientific online mzCloud databases  with a similarity index of 70% or higher.The lipidomic ESI+ and ESI− data sets were processed via Thermo Scientific Lipid search version 4 using the following settings: high energy collision database (HCD) retention time 0.1 min, parent ion mass tolerance (5 ppm), product ion 10 ppm. Alignment method (max), top rank off, minimum m-score 5.0, all isomer peaks, ID quality filter A and B only. Lipid IDs were matched using LipidSearch  using in silico library MS2 level. Corresponding metabolomics and lipidomics pooled QCs samples were used to evaluate and exclude instrumental drifts. Any metabolite/lipid features which has a residual standard deviation (RSD) <25% within the QCs were retained and extrapolated to the data set.T-test was then used to statistically compare compounds in fresh versus macerated bones due the greater sample size in these two groups. Statistical analysis was carried out in R version 4.2.0 (2022-04-22) with the “MixOmics” package [Metabolomics and lipidomics data were normalized by mean values and cube root transformed before applying Pareto scaling. Unsupervised principal component analysis (PCA) was employed to evaluate the differences between fresh versus macerated bones, as well as and potential differences between the three maceration protocols. Hierarchical clustering and heatmaps were then employed to obtain an overview of the different compounds across the groups.  package .PCA for metabolomics assays, both in positive (ESI+) and negative (ESI−) ionization modes, explained 71% of the total variance. No clear inter-individual differences can be observed between the three individuals, as the non-macerated bones form a unique cluster. However, it is possible to see a clear planar separation on the first dimension between fresh and macerated samples .Protocols #1 and #2 (differing uniquely by the duration of the procedure) cluster together in the upper right region of the plot, whereas samples processed with protocol #3, which involves a higher water temperature (87 °C), seem to behave differently and clusters in a different quadrant in comparison with the other two groups, suggesting that the second component may retain variation between the different protocols. The hierarchical clustering shows that similar metabolomic profiles are shared by samples subjected to the same maceration treatments. Samples cluster according to their condition  but also to the specific protocol used, suggesting that all variables  play a role in the final metabolomic profile. As can be seen in the heatmap of metabolites analyzed in ESI+ B, the buCompounds identified and selected with ESI+ after the filtration steps reported in the Methods were then evaluated in detail for their abundance in non-macerated vs. macerated samples and their significance level . As prevt-test reveals that acetyl-CoA (AcCa), ceramides (Cer), phosphatidylethanolamines (PE), and phosphatidylserines (PS) are the classes of lipids significantly more abundant in the fresh samples  as well as the presence of new compound associated with the chemical detergents used during the cleaning process . Changes in the lipidomic profile resulted in being the most complex to interpret, with certain classes being more abundant in the macerated samples and with others being more intense in the fresh ones. Despite the use of animal proxies has been largely employed in forensic sciences, there are several factors that could not be controlled and could have an impact on the outcome of the study. Amongst those there are nutrition, health status and age of the animal. However, due to the source of the bones employed in the experiment  it is reasonable to assume that all animals were similarly fed, treated, shared good health conditions and were slaughtered at the same age. This is supported by the data shown in t-tests run on the single metabolites when comparing fresh and macerated samples further support the hypothesis that maceration can induce not only the loss of specific metabolites, but also introduce specific contaminants depending on the chemicals used. The investigation of the changes associated with the metabolome is popular in food science and can be related to this work. For example, an experiment run to estimate the ideal combination of cooking temperature and duration for Thai chicken bone soup showed an increase in the abundance of free amino acid (AAs) and essential AAs (EAAs) in the bone broth following boiling, with an associated and expected decrease in their concentration in the bone tissue [When considering the metabolites, the PCA score plot A revealee tissue . This fuPrevious works conducted on several soft tissues in forensic science highlighted the great potential for metabolomics to reveal information on PMI ,23,24. DDespite single lipid compounds do not seem to change consistently according to the different protocols used, certain lipid classes show a more regular pattern when considering either fresh or macerated samples. The separation between pre- and post-treatment samples, as well as those existing between different protocols, are better appreciated in ESI+ mode. In fact, when investigated in ESI−, the modifications induced by the maceration processes do not show clear distinctions between any of the groups, including fresh vs. macerated. In a previous study, Nawar et al.  showed tIt is difficult to determine if a shortening of the lipid chains was directly linked with the maceration processes due to the experimental set up. However, the observed modification in the lipidome could be potentially linked to the fact that the bones used in the experiment were fresh and had some remaining adhering soft tissues and blood on their surface. When macerating them at temperatures above 50 °C, some lipid classes originated from these remaining soft tissues may have been released in the water bath and may have eventually adhered onto the periosteal surface, increasing therefore the abundance of these compounds specifically in the macerated bones, particularly at higher temperatures 87 °C, . Table 2Despite the limited body of research in the application of lipidomics analyses to forensics, several studies have shown the potential of lipidomics for PMI estimation from several tissues. Wood and Shirley  sampled The present study provides preliminary results on the changes in both metabolite and lipid profiles induced by some of the maceration procedures commonly used in HFTFs to re-move the superficial soft tissues prior to long term bone storage. The results presented here on the metabolomic and lipidomic alteration of the profiles of chemically and thermally treated bones in comparison with their non-treated counterparts highlight the importance of minimizing the loss of the biomolecular integrity when using soft tissue removal protocols for skeletal preparation. We have shown that these treatments induce not only the loss of endogenous metabolites, but also introduce contaminations caused by the chemicals used during these processes. The high-water temperature and the type of chemical used in the water bath are the main variables able to affect the bone metabolome of treated bones. However, also less harsh protocols involving lower water temperatures and shorted durations can affect irreversibly the bone metabolome. The bone lipidome seems to be less affected by the submersion in lower temperatures and more impacted when higher temperatures are used. Furthermore, it may be susceptible to potential contaminations introduced by the presence of residual soft tissues and blood in the water bath. The employment of various protocols in different institutions drastically reduces the possibility to compare results from experiments on material originated from different HFTFs, with clear negative implications for the forensic community. Therefore, a more standardized way of processing bones should be sought after to preserve the invaluable potential that human donated cadavers offer to science. However, treated bones may not be suitable for any metabolomic nor lipidomic analysis aimed at addressing PMI for forensic applications, and further analyses on human bones may validate these findings. Alternatives, such as performing small bone samplings prior to the maceration of the remains to allow future molecular analyses on those samples, should be considered by HFTFs to maximize the amount of information recoverable from bones when maceration cannot be avoided. Nonetheless, cooperation between the HFTFs is a central necessity in order to let the field of forensic science grow and, with it, the breadth of studies based on their skeletal collections.


The digital health sector has experienced rapid growth over the past decade. However, health care technology stakeholders lack a comprehensive understanding of clinical robustness and claims across the industry.This analysis aimed to examine the clinical robustness and public claims made by digital health companies.A cross-sectional observational analysis was conducted using company data from the Rock Health Digital Health Venture Funding Database, the US Food and Drug Administration, and the US National Library of Medicine. Companies were included if they sell products targeting the prevention, diagnosis, or treatment phases of the care continuum. Clinical robustness was defined using regulatory filings and clinical trials completed by each company. Public claims data included clinical, economic, and engagement claims regarding product outcomes made by each company on its website.r2=0.02), clinical robustness and total funding (r2=0.08), or clinical robustness and company age (r2=0.18).A total of 224 digital health companies with an average age of 7.7 years were included in our cohort. Average clinical robustness was 2.5  with a median score of 1. Ninety-eight (44%) companies had a clinical robustness score of 0, while 45 (20%) companies had a clinical robustness score of 5 or more. The average number of public claims was 1.3 ; the median number of claims was 1. No correlation was observed between clinical robustness and number of clinical claims (Many digital health companies have a low level of clinical robustness and do not make many claims as measured by regulatory filings, clinical trials, and public data shared online. Companies and customers may benefit from investing in greater clinical validation efforts. The digital health sector has grown rapidly over the past decade ,2. ThereAlthough growth is apparent, the ability to measure impact is not. Several studies have highlighted the need for greater clinical validation ,5 and foAs a result, the literature is often too narrow or too broad to enable an understanding of clinical impact. In addition, no studies have examined both clinical rigor and public claims made by companies. To address these limitations, we sought to comprehensively examine the topic of clinical robustness in digital health companies by using a more comprehensive definition of clinical rigor and examining companies’ public claims across the most in-depth database of US-based digital health companies. These findings provide additional context for all stakeholders in health technology that rely on a more accurate characterization of digital health solutions.Companies were identified using the Digital Health Venture Funding Database maintained by Rock Health Inc, a digital health venture fund and advisory firm, which has been used in prior studies -27. The Total venture funding, clinical area(s) of focus, care continuum phase(s), and customer data were collected for each company. Clinical areas represented 1 of 20 specific clinical domains . Care continuum phase was defined according to the following: prevention, diagnosis, or treatment. Customer type referred to the category of buyer for a company’s products, such as payer , biopharma , and medical devices . Companies can be categorized into multiple categories . Company variables were gathered from the Digital Health Venture Funding Database, which Rock Health maintains using publicly available information such as company websites, press releases, and US Securities and Exchange Commission filings. Data for companies were collected through August 3, 2021.The number and type of claims, defined as unique quantitative statements about product outcomes, made on a company’s website were collected in the following categories: clinical, economic, and engagement. The definitions of these claim types are detailed in We collected regulatory data from the Food and Drug Administration (FDA), including the number of 510(k), De Novo, and premarket approval filings (where the company was listed as the “Requester” on fda.gov). In addition, we collected the number and type of clinical trials by searching ClinicalTrials.gov where the company was listed as “Sponsor / Collaborator.” Data on both FDA filings and clinical trials were collected between July 1, 2021, and September 2, 2021, through a combination of web scraping and manual searching. Data collection on claims was completed by at least 3 authors, with blinded cross-review to ensure consistency in data collection.A “clinical robustness” score was calculated for each company, defined as the sum of the number of regulatory filings and clinical trials. Each regulatory filing and clinical trial was weighted equally in the calculation.All data were stored in Microsoft Excel (Microsoft Corp), where all descriptive and statistical analyses including coefficient of correlation between variables were calculated.There were 224 companies in the cohort, with an average age of 7.7 years. Collectively, these companies have raised a total of US $8.2 billion in venture capital funding since 2011. The companies spanned 3 phases of the care continuum, with 25 offering solutions for prevention, 106 offering solutions for diagnosis, and 110 companies offering solutions for treatment . Some coThe average clinical robustness score for all companies was 2.5 . The median clinical robustness score was 1, with 98 companies (44%) having a score of 0 and 34 companies (15%) having a score of 1. Diagnosis companies had the highest average clinical robustness scores (2.8), followed by treatment companies (2.2), and then prevention companies (1.9).The average clinical robustness score of companies that sold to employers was 3.1, compared to 2.0, 2.2, and 2.7 for those companies that sold to payers, consumers, and providers, respectively . FifteenThe average number of total claims for all companies was 1.3  with the median number of total claims equal to 1  . The medr2=0.02), clinical robustness and total funding (r2=0.08), or clinical robustness and company age (r2=0.18). In addition, there was no strong correlation between clinical areas that had higher clinical robustness scores and clinical areas where companies have received higher average funding (r2=0.07).There was no correlation between clinical robustness and number of clinical claims  that had a score of 5 or more, suggesting a small population of rigorously tested solutions . AlthougAlthough average clinical robustness was quite low across the population, there was significant variation across clinical areas  . This maThe average and median number of all claims was also low. These findings indicated that digital health companies largely did not share outcomes publicly. This may have reflected a desire to keep this data private, but more likely represented a lack of meaningful analyses of any impact  since this data could be used as a competitive differentiator if shared publicly. Separately, we identified 32 companies that had one or more clinical claims and a clinical robustness score of 0. These findings may suggest a disconnect between marketing and evidence. Additional future research could examine the links between public claims and clinical trials or regulatory filings .Companies that sold to employers made more claims and had higher clinical robustness scores compared to other customer types . This maOur definition of clinical robustness was limited to clinical trials and regulatory findings, which remain proxies for effectiveness and require companies to register their activities. However, we believe these elements offer a better estimation of clinical rigor than publications, which can lag in timing and may not always relate to clinical outcomes. We chose to focus on clinical trials and regulatory filings because they were publicly available data and are generally undertaken to demonstrate an impact on clinical outcomes. Our approach to measuring clinical robustness equally accounts for clinical trials that demonstrate a technology does and does not work. Additionally, our data collection methodology could have missed clinical trials that were not registered on ClinicalTrials.gov or regulatory filings that were submitted to the FDA under an individual’s name rather than the company name . Future research could incorporate condition- or disease-specific metrics of effectiveness that are then standardized across clinical areas to provide a more accurate measure of clinical impact.In addition, our cohort only included venture-backed companies above US $2 million in funding. This may have resulted in selection bias by excluding both ends of the spectrum . Although our population excludes these companies, we believe that our sample represents the most comprehensive assessment of outcomes or claims across the digital health landscape and encompasses the majority of funded companies and activity in this space. As a point of reference, the average seed stage deal size in 2021 was US $3.5 million, 75% higher than our minimum funding threshold .Despite the hundreds of digital health companies targeting the myriad of needs across the care continuum, clinical robustness and public communication of claims remains low across much of the sector. These results highlight a significant opportunity for companies to differentiate themselves and for customers to demand greater validation for the products and services they purchase.


Non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHBA) are the metabolites of fat mobilization initiated by negative energy balance (NEB) during the perinatal period in dairy cows, which have an adverse effect on cell physiology of various bovine cell types. The aim of this study was to explore the biological roles of NEFA and BHBA on provoking oxidative stress and inflammatory responses in bovine mammary epithelial cells (BMECs). RNA sequencing analysis showed that there are 1343, 48, and 1725 significantly differentially expressed genes (DEGs) in BMECs treated with NEFA, BHBA and their combination. GO functional analysis revealed that the DEGs were significantly enriched in “response to oxidative stress” and “inflammatory response”. Further study demonstrated that NEFA and BHBA elevated the malondialdehyde (MDA) and reactive oxygen species (ROS) accumulation and reduced the total superoxide dismutase (T-SOD) and glutathione peroxidase (GSH-Px) activity to cause oxidative stress. In addition, expression of inflammatory markers  were increased after NEFA and BHBA stimulation. Mechanistically, our data showed that NEFA and BHBA activated the MAPK signaling pathway. Collectively, our results indicate that NEFA and BHBA induce oxidative stress and inflammatory response probably via the MAPK signaling pathway in BMECs. Transition dairy cows, from late gestation to early lactation, are more likely to experience metabolic changes  and infectious  diseases ,2. DurinNEFA could act as signaling and energy-rich molecules and have an adverse effect on the cell physiology of various bovine cell types. Previous research confirmed that high concentrations of NEFA could induce apoptotic damage and inflammation in bovine granulosa cells and hepatocytes ,10,11,12During negative energy balance, high plasma concentrations of NEFA and BHBA can be absorbed by the mammary gland, thereby augmenting the response of mammary epithelium to metabolic stress. BMECs participate in the first line of mammary gland defense against external stimulus. Previous studies have found that NEFA could induce inflammatory response and oxidative stress in BMECs ,17. Howe2. The medium was changed every 48 h. Bovine mammary epithelial cell line  were a gift from Dr. Youping Sun. Cells were cultured in DMEM/F12 medium  supplemented with 10% FBS  and 200 U/mL of penicillin and streptomycin , and incubated at 37 °C in an atmosphere of 90% humidity and 5% COBMECs were treated with NEFA , BHBA , and their combination ,19. BefoThe levels of total superoxide dismutase (T-SOD), malondialdehyde (MDA), and glutathione peroxidase (GSH-Px) were measured in the supernatant of BMECs using a biochemical analysis kit  according to the manufacturer’s protocol. The optical density of MDA, SOD, and GSH-Px was determined by a microplate reader at absorption wavelengths of 532 nm, 450 nm, and 412 nm, respectively.6 cells and then treated with NEFA, BHBA, and their combination. The intracellular level of ROS was determined by a corresponding commercial kit . The method was based on the ROS-sensitive probe DCFH-DA, which can enter cells freely through living cell membranes and be hydrolyzed into DCFH by intracellular esterases, and DCFH is easily oxidized to DCF (dichlorofluorescein) which is a strong green fluorescent substance that can be measured at excitation and emission wavelengths of 500 and 525 nm. BMECs were incubated with 10 µM of DCFH-DA in the dark for 30 min at 37 °C, then resuspended in PBS and immediately analyzed by a fluorescence microscope ). ROS fluorescence intensity was quantified by Image J software .The BMECs were seeded into six-well plates at 1 × 10The level of nitric oxide (NO) was measured by a corresponding commercial kit , according to the manufacturer’s instructions. After NEFA and BHBA treatment, BMECs were incubated with the fluorescent probe DAF-FM DA at 37 °C for 30 min, then washed in PBS and analyzed by a fluorescence microscope ).The cells were treated as described above. The supernatant levels of tumor necrosis factor-α (TNF-α), interleukin (IL)-6, and IL-1β were measured using a commercially available enzyme-linked immunosorbent assay kit  according to the manufacturer’s instructions. The results were measured spectrophotometrically at a wavelength of 450 nm.®Premix Ex Taq™  on an Applied Biosystem 7500 HT Sequence detection system. The PCR program consisted of one cycle at 95 °C for 30 s, 40 cycles at 95 °C for 5 s, and 60 °C for 34 s, and fluorescence signal collection at 60 °C. All the primers used in this study were shown in –ΔΔCt method.TRIzol reagent  were used to extract total RNA of the BMECs. The purity and quantity of total RNA were detected by NanoDrop 1000 spectrophotometer . DNase I and the Prime Script RT Master Mix kit  were used for DNA removal and cDNA synthesis, respectively. Real-time quantitative PCR was performed using SYBR http://asia.ensembl.org/Bos_taurus/Info/Index (accessed on 5 May 2021) using HISAT2. 2.4. The mapped reads of each sample were assembled by using StringTie (v1.3.1) in a reference-based approach. DESeq2 software were used for RNAs differential expression analysis. Differentially expressed genes (DEGs) were identified based on the parameter of false discovery rate (FDR) below 0.05 and absolute fold change ≥2. Hypergeometric tests were used to evaluate the enrichment score. A p-value ≤ 0.05 indicated significantly enriched GO terms and KEGG and Reactome pathway. The raw data were submitted to the National Center for Biotechnology Information (NCBI) under BioProject accession number PRJNA869860 and PRJNA880749. Three samples were selected from the control, NEFA, BHBA, and their combination treatment group for acquiring transcriptome sequences. RNA extraction, quality control, and sequencing were performed as described previously . Reads wBMECs were harvested after NEFA and BHBA treatment. The total BMECs protein were extracted and quantified as described previously . Sodium t test was used to compare the two sets of data.GraphPad Prism 6.01 software  was used for statistical analyses. All values are shown as the mean ± standard error of the mean (SEM) from three repeats of each experiment that was run in triplicate. The comparisons of more than two groups were determined using one-way analysis of variance (ANOVA) followed by Tukey’s test. A Student’s To clarify the cytotoxic molecular mechanisms of NEFA and BHBA in the BMECs, transcriptome sequencing and analysis were performed. Analysis of DEGs under NEFA treatment caused a total of 1343 DEGs, of which 708 genes were upregulated and 635 genes were downregulated A. FollowAs shown in Based on the GO enrichment analysis, we further assessed the role of NEFA and BHBA on inducing oxidative stress in BMECs. As shown in The effect of NEFA and BHBA on inducing inflammatory response in BMECs was also further analyzed. Results showed that the level of proinflammatory cytokines  was considerably increased under stimulation with NEFA, BHBA, and their combination when compared with the control group A–F. SimiAccording to the KEGG pathway analysis, the regulation of MAPK signaling pathway may be the most likely underlying mechanism of oxidative stress and inflammation induced by NEFA and BHBA in BMECs A–C. The In the present study, we studied the molecular response of BMECs to high concentrations of NEFA and BHBA, with a focus on oxidative stress and inflammatory response. The experimental results here demonstrated that NEFA, BHBA, and their combination treatment (1) induced oxidative stress, as indicated by increased MDA and ROS accumulation and decreased SOD and GSH-Px activities, (2) induced the inflammatory reactions, characterized by significant elevation of cytokine expression, and (3) activated the MAPK signaling pathway. These findings illustrated the effects and common molecular events of NEFA and BHBA on triggering oxidative stress and inflammatory response in BMECs.High plasma concentrations of NEFA and BHBA during the time cows face the challenge of NEB could be loaded and absorbed by the mammary gland, thereby inducing a state of oxidative stress ,23. OxidStudies showed that oxidative stress and inflammatory response are closely connected and could synergistically contribute to metabolic and infectious disorders in perinatal dairy cows; oxidative stress-mediated inflammation has long been identified as triggers of miscellaneous inflammatory diseases ,28,29,30The MAPK pathways, represented by p38MAPK, JNK, and ERK, are activated by a diverse array of extra- and intracellular stimuli including inflammatory cytokines and oxidative stress ,35. PrevIn summary, our results provide new knowledge on the role of NEFA and BHBA in oxidative stress and inflammatory response of bovine mammary epithelial cells. In the current study, both NEFA and BHBA induced oxidative damage and inflammation, and their combination is endowed with a synergistic effect . Further


Given the importance of data safety for psychology, the present study investigated the influence of data leaking scandal on campus customers’ financial consumption behaviors at intelligent tourism platforms in China, and explored the roles that individual characteristics play in this process by focusing on a set of participants from colleges. Data were collected through sending out an online questionnaire, where respondents were asked to finish a series of questions about their background information, their trust, future consuming intention, and defensive behaviors toward intelligent platforms. After they finished these questions, a short description about an online tourism platform leaking customers’ personal information was presented to the respondents, following which they were asked to report about their future consuming intentions and defensive behaviors again. In total, 236 participants of college students and teachers were recruited. Paired samples mean comparison showed that after the stimulus was presented, the respondents had a significant decrease in future financial consumption intention, and a significant increase in defensive behaviors toward online tourism platforms due to risks perceived. Multiple regression analysis was conducted subsequently to investigate individual characteristics that may account for part of the decrease (increase) in consuming intention (defensive behaviors). Results showed that, customers with higher level of trust and monthly income, as well as older customers, tend to experience higher level of decrease in consuming intention, and increase in defensive behaviors. These findings highlighted the importance of online tourism platforms guaranteeing data security of their customers. In the era of business intelligence, data has been accepted as a new factor of production, and the correlation between personal data holding and business competitiveness has become more and more significant . The “biIn 2020, China’s Ministry of Industry and Information Technology punished 15 APPs for collecting personal information without permission, excessively claiming permission, and sharing it with third parties. Obviously, APP’s improper acquisition and inadequate protection of data result in massive data leakage, which seriously threatens the security of users’ personal information. The issue of online platforms leaking customers’ private data  has attracted increasing attention, as it may cause serious consequences such as reduction in customers’ consumption intentions to continue visiting these platforms see . As a maNevertheless, previous studies rarely looked into how a data leaking scandal of online tourism platforms may affect customers’ financial consumption behaviors. Specific data development of current tourism platforms will be briefly discussed in the literature review. “Smart tourism” is an important development direction in China . At presThe diversified functions and convenience offered by the online tourism platforms makes it a priority for most potential customers planning to travel to get more help from the Internet when buying airline tickets, booking accommodation or researching attractions . TherefoThis paper argues that the financial risks of tourism platforms are underestimated in China. At present, with the continuous development of Internet and communication technology in China, the way of tourism information processing and transaction has changed from traditional face-to-face to electronic, a large number of financial traces related to tourism transactions therefore have been left. 1 which is a Chinese popular survey platform. Finally, 236 participants from colleges in Heilongjiang and Guizhou Provinces were recruited. This sample size is decided beforehand by using G*power, that the sample size to detect an effect of 0.2 with 90% power in t-tests is 207. The sample was balanced in gender , was diverse in age, with participant from age groups (1) under 19 (N = 5), (2) between 19 and 30 (N = 90), (3) between 30 and 40 (N = 61), (4) between 40 and 50 (N = 57), and (5) above 50 (N = 23) years old. The sample also includes participants with different levels of monthly income, such as (1) below 3,000 (N = 33), (2) between 3,000 and 6,000 (N = 70), (3) between 6,000 and 9,000 (N = 70), (4) between 9,000 and 12,000 (N = 34), (5) between 12,000 and 15,000 (N = 18), and (6) above 15,000 yuan (N = 11). In the questionnaire, the respondents were firstly asked to finish questions about their demographic information, and several questions about their trust toward online tourism platforms, intention to continue consuming in online tourism platforms, and defensive behaviors due to perceived risks toward online tourism platforms. After finishing these questions, the respondents were presented a description about an online tourism platform leaking customers’ personal data . After this stimulus was presented, the respondents were asked to answer questions about their future intention and defensive behaviors toward online tourism platforms again. Such process design was inspired by An online survey questionnaire was prepared and conducted in WenjuanxingFuture Consuming Intention was measured with five items tapping customers’ willingness to use tourism platforms in the future, such as, In the future, I am willing to continue consuming in online tourism platforms. These items are adapted from Questionnaire measuring Customers’ Promotion-focused Behavior from Defensive Behaviors toward online tourism platforms was measured by five items reflecting their requests and actions to prevent tourism platforms from getting further contact with them, such as, In the future, I will ask online tourism platforms to stop using my personal information. These items were adapted from the questionnaire measuring Customers’ Prevention-focused Behaviors by Trust toward Online Tourism Platforms was measured with five items about their positive beliefs about the reputation of the tourism platform, such as I believe online tourism platforms are honest and trustworthy. These items were developed based on the questionnaire measuring customers’ Trust in Online Retailing from No, I totally disagree) to five . Therefore, a higher score on the construct reflect a higher level of the measured traits.Customers’ t-tests, to compare the mean scores on Future Consuming Intention and Defensive Behaviors before and after the stimulus was presented. Cohen’s d was reported to indicate whether a significant effect was small (> 0.2), medium (> 0.2 and < 0.5), or large . On the contrary, there was an increase in the mean scores of Defensive Behavior before and after the stimulus is presented, also with a moderate correlation between the two scores .Descriptive statistics and correlations between studied variables are present in p < 0.001), with a larger effect size (Cohen’s d = 0.96). On the contrary, the customers’ Defensive Behavior increased significantly after the stimuli was presented , with a large effect (Cohen’s d = 0.91).Results of mean comparisons and regression analysis are presented in p < 0.001), and the increase in Defensive Behavior . Customers’ age also had significantly positive effects on the decrease in Future Consuming Intention , and the increase in Defensive Behavior , indicating that older customers are more tended to decrease their intentions and increase their defensive behaviors after knowing the scandal. In addition, customers’ Monthly Income had significantly positive effects on the decrease in Future Consuming Intention , and the increase in Defensive Behavior , indicating that customers with higher income are more likely to reduce their intention and take defensive actions toward data leaking. These models explained 23.6% of the variances in Decrease in Consuming Intention, and 24.3% of the variances in Increase in Defensive Behaviors.To further investigate factors affecting the decrease in customers’ Future Consuming Intention and the increase in their Defensive Behavior, the difference scores were made by subtracting lower scores from higher scores, and were included in multiple regressions as dependent variables. The results showed that customers Trust toward tourism platforms had significantly positive effects on the decrease in Future Consuming Intention , future research may therefore include these characteristics as well and discuss their roles in shaping customers’ reactions toward a data leaking scandal.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.MF and BH performed material preparation, data collection, and analysis. MF and HW wrote the first draft of the manuscript. YT drafted and edited the revised manuscript. All authors read and approved the final manuscript and contributed to the study conception and design and all authors commented on previous versions of the manuscript.


Prostate cancer (PC) is the fifth leading cause of death in men globally. Measurement of the blood PSA level is still considered the gold-standard biomarker test for PC despite its high rate of delivering false positives and negatives that result in an inappropriate medical response, including overtreatment. We collected extracellular vesicles (EVs) from the blood plasma of PC patients with organ-confined, extracapsular-invading, and seminal vesicle–invading tumors and from healthy subjects. We examined the protein, mRNA, and miRNA content of these EVs using mass spectrometry (MS), a human PC PCR array, and a miScript miRNA PCR array, respectively. The proteomic analysis showed distinct groups of proteins that are differently expressed in each group of patients, as well as in healthy subjects. Samples from healthy subjects and each tumor type were used for both mRNA and miRNA arrays. The mRNA analysis showed distinct groups of mRNAs that were overexpressed in healthy or in one of the three tumor types but not in the EVs of the other groups. The miRNA analysis showed distinct groups of miRNAs as well. The fold of regulation in the expression of the identified mRNA and miRNA of each stage of the disease from healthy subjects showed that various mRNAs and miRNAs could discriminate the disease stage. Overall, our data suggest many molecular marker candidates for distinguishing between healthy subjects and PC patients using the cargo of circulating vesicles, as well as markers to discriminate between the different tumor types. Once verified, these markers might have a diagnostic value for PC. Prostate cancer (PC) is the fourth highest incidence cancer globally, with 1.1 million cases, and is the fifth leading cause of cancer mortality in men  (Serum prostate-specific antigen (PSA) levels are considered the standard biomarker test for PC . AlthougExtracellular vesicles (EVs) are small membrane compartments that are shed from both malignant and normal cells that contain proteins, RNAs, and DNAs . OriginaFree-circulating proteins are the biomarkers most widely used across multiple cancers . In recemiRNAs are small non-coding RNAs that average 22 nucleotides in length. They are involved in a variety of functions including the suppression of translation or mRNA degradation through the RNAi pathway –27. miRNmRNA has promise as an indicator of the PC outcome and the examination of its expression in PC tumor tissue can be shown to predict lethal and metastatic cancer progression in men with localized PC . When exHerein, we examined the contents of EVs extracted from patient blood plasma and compared data on their proteomics, mRNA, and miRNA profiles to generate a library of potential markers for PC diagnosis and prognosis. These libraries of data were generated from four groups of six subjects for each group for the proteomic studies and three subjects in each group for mRNA and miRNA microarray studies. The groups are one healthy group, an organ-confined PC (OC) group, an extracapsular-extending PC (EC) group, and a seminal vesicle–invading PC (SI) group. Our data show that blood plasma EV cargo can be identified to distinguish between a PC patient and a healthy subject of the same age and can also be used to discriminate between PC patients at different stages of the disease. Upon further validation, EVs could provide simple, non-invasive, high-specificity markers to diagnose PC and monitor its progression.2, and shipped on dry ice. Upon receiving the samples, they were kept in liquid N2 and thawed at the time of EV collection. Plasma collection was performed using lavender-topped K2EDTA tubes. Tumor subject samples, accompanied by full clinical and demographic information, were obtained, thanks to the Ontario Institute of Cancer Research tumor bank. The age range for the patients is (60–64) and (65–69) (the patient’s age has been supplied by the OICR tumor bank as a range of 5 years to preserve the patient’s privacy). The other demographic information includes the patient’s sex. In this study, all the patients are men, and, as for the vital status, all patients were alive at the time of receiving the samples. Healthy subject samples were obtained, thanks to St. Joseph’s Healthcare Hamilton, Ontario, Canada. The subjects were chosen with matching age to the patients and with no previous record of any type of cancer or major health concern. This study received ethical approval from the Hamilton Integrated Research Ethics board.Four groups of six subjects were used for the proteomics study, and another four groups were used for miRNA and mRNA studies with three subjects in each group; the four groups included one healthy group and one group for each of three different PC types. These three tumor types include organ-confined tumors (OC), tumors exhibiting extracapsular extension (EC), and tumors exhibiting seminal vesicle invasion (SI). The plasma samples for all patients have been obtained before the prostatectomy procedure, chemotherapy, or radiation treatments. The plasma samples were supplied in 1 ml/vials, previously frozen in liquid Nxg for 10 min at 4°C, 12,000 xg for 20 mins at 4°C, and 100,000 xg for 2 hours at 4°C. Pelleted EVs were collected from ultracentrifuge tubes and suspended in 200 µl of phosphate-buffered saline (PBS).Differential centrifugation, as described in –15, 37, EVs collected from PC patients and healthy subjects were subjected to nanoparticle analysis using the NanoSight LM14C with an infusion rate of 80 and after being diluted 1:10.A 10% SDS-PAGE gel was loaded and run with 100 µg of vesicle protein from 1 ml of blood plasma from each subject sample and stained with Coomassie blue (BioRad). After visualization, sample bands were excised from the gel and subjected to mass spectrometry (MS)-based proteomics. Excised bands were dehydrated in 50% acetonitrile (ACN) and reconstituted in 50 mM ammonium bicarbonate containing 10 mM Tris-2-carboxyethyl phosphine before being vortexed at 37°C for 1 h. Sample alkylation was achieved with chloroacetamide at a final concentration of 55 mM and 1 µl of trypsin to perform digestion. Peptides were extracted in 90% ACN and, after a number of steps, were subjected to label-free quantification MS experiments using collision-induced dissociation in a linear ion trap. PEAKS software (Bioinformatics Solutions Inc.) was used to convert MS data to peaks lists and then to fit the data to the Human1302S database assuming trypsin to be the digestion enzyme to perform MS/MS spectra analysis. A parent ion mass tolerance of 10.0 PPM (monoisotopic) and a fragment ion mass tolerance of 0.0100 Da (monoisotopic) were permitted during the database search. The variable modifications: the deamination of asparagine and glutamine; the oxidation of methionine; carbamidomethylation of cysteine; and the phosphorylation of serine, threonine, and tyrosine were all specified in PEAKS software. The protein scored and expectation values were used to determine protein match probabilities, and resolved protein identities were considered to be correct when it contained four unique peptides and had a score higher than the identity threshold at p < 0.05.An miRNeasy Minikit (Qiagen) was used to isolate RNA from vesicle suspensions containing 10 µg/µl RNA-grade glycogen (Thermo Scientific). Isolated RNA received 290 µl of ice-cold ethanol and was incubated overnight at -20°C to concentrate it. RNA was subjected to centrifugation at 10,000 rpm for 15 min and was rinsed twice in ice-cold 75% ethanol before the pellet was air-dried for 5 min and resuspended in 8 µl of RNAse-free water. Nanovue  was used to determine RNA concentration and quality.https://geneglobe.qiagen.com/us/analyze).A miScript II RT kit (Qiagen) was used to perform reverse transcription. The preamplification of miRNAs prior to quantification was required to accurately assess their expression because of their low abundance in EVs. A miScript Preamp PCR kit (Qiagen) in conjunction with the miScript Human Prostate Cancer PreAMP PCR primer mix was used to perform multiplex PCR-based preamplification reactions. miScript miRNA PCR array with a Human Prostate Cancer Panel (Qiagen) was used to measure miRNA expression profiles and qPCR reactions were performed on the ABI ViiA 7 (Applied Biosystems). Six control small nuclear RNAs: SNORD61, SNORD68, SNORD72, SNORD95, SNORD96A, and RNU6B/RNU6-2 were used to normalize the expression values of miRNAs, and differential expression was calculated using the ΔΔCt method. Raw data were analyzed using the miScript miRNA PCR Array Data Analysis Website (2-Profiler PreAmp cDNA Synthesis Kit (Qiagen) was used to perform first-strand synthesis, and the resulting cDNA templates were preamplified with RT2 PreAMP cDNA Synthesis Primer, Human Prostate Cancer panel (Qiagen). The RT2 Human Prostate Cancer PCR array (Qiagen) measured the mRNA expression profiles and five endogenous controls—ACTB, B2M, GAPDH, HPRT1, and RPL–0—were used to normalize expression values. The ΔΔCt method was used to calculate differential expression, and raw data were analyzed using the RT2 Profiler PCR Array Data Analysis Website .An RTThe NanoSight showed that EVs from the plasma of both PC patients and healthy subjects , extracapsular-extension (EC), and tumor-exhibiting seminal vesicle invasion (SI) with six samples for each stage. Additionally, EVs were collected from the blood plasma of six healthy men with matching age (50–65) and had no previous history of cancer or benign tumor. To further illustrate protein differentiation, we have created Venn diagrams that show the number of proteins that are unique to each subject type and the ones that are shared by multiple subject types  test measures both circulating tumor DNAs and RNAs to diagnose potential non-small-cell lung cancer patients.The potential of these markers becomes even more powerful when one considers that they may be measured together, as there are not only multiple proteins exclusively associated with a particular patient’s status but also multiple mRNAs and miRNAs. Measuring multiple markers together will always create a more accurate picture of the truth during diagnosis, leading to fewer false positives and negatives and to pinpointing the nature of the tumor and its progression. Because EVs can be isolated from blood, they are also very valuable as a diagnostic tool due to their collection and analysis being less invasive than biopsy. For this reason, blood plasma EVs, along with EVs collected from urine as described in Wang et al. , have thKim et al.  found pivia LBP, which is known to interact with VEGFA, and CRP, which, in addition to interacting with the protein FCN2, is coexpressed with APOH, APOA2, and LBP. Textmining for proteins that appear often in paper abstracts together suggests associations between CRP and the translated mRNAs of VEGFA, AKT1, CASP3, TP53, and B2M. Additionally, in a separate network, textmining reveals that CD5L, one of our top candidate protein biomarkers, is associated with the translated protein of IGJ, one of our top candidate mRNA biomarkers. Together, our top mRNA and protein candidates have known involvement in multiple cancer KEGG pathways, as well as platinum drug resistance and diabetes  #13-288-T. The study was performed in accordance with the Declaration of Helsinki. The patients/participants provided their written informed consent to participate in this study.Author Contributions: KA-N designed the project, supervised the study, analyzed the data, and wrote the manuscript. JC, SH-N. Prepared samples for proteomics analysis, and performed mRNA, miRNA arrays experiments, and helped in preparing the manuscript for publication. PM helped in preparing the manuscript for publication. All authors contributed to the article and approved the submitted version.


Athene cunicularia Molina, 1782) in Llano La Soledad, Galeana, Nuevo Leon in northern Mexico, by considering prey type, numerical percentage, weight, weight percentage, frequency of occurrence percentage, and IRI percentage. The study compared data from three winters  by analyzing 358 pellets, identifying 850 prey items. Invertebrates constituted 90% of prey items, which mostly included insects (85%); beetles were the most common insects found in pellets (70%). Vertebrates made up 84% of consumed weight, of which 83% were mammals. Most of the mammals were cricetid rodents (41%). Niche breadth based on the numerical and weight percentage confirmed the Burrowing Owl as a generalist species with mean values per year ranging between 0.65 and 0.82. Additionally, there was a strong association between the weight of rodent species in winter. This association was mainly driven by changes in composition and frequency of these prey species during the second winter, probably caused by high annual rainfall. The second season also showed a statistically significant narrower niche (Ro = 0.96) and the smallest overlap (0.45 vs. 0.76) among the three winters.The dietary niche breadth of the Burrowing Owl was determined ( Athene cunicularia Molina 1782) populations are distributed from southwest Canada, through the western and central USA , and Mexico. However, most northern populations migrate to the southern USA and Mexico (Cynomys ludovicianus) and Mexican Prairie Dogs (C. mexicanus) colonies in Mexico, using their burrows for protection against predators as well as for nesting , Silky Pocket Mouse (Perognathus flavus), and Merriam’s Kangaroo Rat (Dipodomys merriami), represented 98% of prey counted in the collected pellets  consumed during dry years but more birds consumed during wet years  in northern Mexico . Our hypotheses are (1) that the diet composition in years with high rainfall will be different than in drier years, (2) that differences in rainfall will also affect diet niche breadth.Llano la Soledad is a plains habitat located in the northeastern Mexican state of Nuevo León, municipality of Galeana, within the Grassland Priority Conservation Area “El Tokio” . This arPellets were collected every other day at active burrows located along 20 random transects of 1 km × 200 m, representing an area of 400 ha . We traveled the transects daily from the first week of October through the first week of March over three winters  to collect population density data.Each pellet was analyzed and quantified according to the mentioned by The percentage of frequency of occurrence (FO%) was calculated for each taxonomic level of prey  by dividing the number of pellets, in which each kind of item was found, by the total number of pellets collected. The numerical percentage (N%) was calculated by dividing the number of items in each prey category by the total number of prey items found in all pellets. In both cases, it was multiplied by 100 to convert to percentage. The weight percentage (W%) was estimated as the total weight of each prey taxon divided by the combined estimated total weight of all prey taxa, multiplied by 100. The index of relative importance was calculated as: IRI = (N% + W%) FO%, where N% = numerical percentage, W% = weight percentage, and FO% = percentage of frequency of occurrence . The IRIAll protocols were performed according to the guidelines adopted by the ethics committee of the Facultad de Ciencias Biológicas of the Universidad Autónoma de Nuevo León. However, to comply with Mexican regulations, we obtained a permit (SGPA/DGVS/01588/10) granted by the Secretaría del Medio Ambiente y Recursos Naturales/Subsecretaría de Gestión para la Protección Ambiental/Dirección General de Vida Silvestre.For each winter season, we estimated niche breadth with 95% confidence intervals (CIs) using Smith’s measure (FT) , conside2 contingency tests , and Orthoptera  , represeSmith’s measure of niche breadth was wide, corresponding to a generalist species. These values are consistent for the three winters, so there is no statistically significant difference in the numerical percentage between winters  than the overlap found between the first and third winter (Ro = 0.86) and between the second and third winter (Ro = 0.83). Regarding the weight percentage, this index showed greater overlap between the first and third winter (Ro = 0.76), than between the first and second winter (Ro = 0.45), and the second and third winter (Ro = 0.45).2 = 5.82, df = 4, p < 0.0001, ϕc = 0.18) and a moderate association for families  and rodent species . We found a weak association between winters and the number of elements for invertebrate classes  and a moderate association for orders  and families .Based on the ϕc values, there was a weak association between the winters and the number of items for vertebrates classes , a moderate association for families , and a strong association for rodent species .In terms of weight, there was a weak association between winters and weight for vertebrates classes (χXerospermophilus spilosoma) and Mexican Woodrats (Neotoma mexicana), as well as decreased consumption in Merriam’s Kangaroo Rat during the same period , orders , and families .The strong association observed in the rodents species during the second winter season was due to the greater consumption of Spotted Ground Squirrel  were the most common, abundant food items, corroborating previous studies, showing that overwintering Burrowing Owls feed mainly on arthropods and small mammals . Invertee.g., Beetles were the most frequently consumed insects (56%), with an average variation of 11% during the years considered for the study. Beetles are not frequently observed as prey in North America, and were mostly recorded during the breeding season 39–54%; , whereasWe found that the Western Harvest Mouse was the most common rodent prey (19%), followed by the Silky Pocket Mouse (15%), Deer Mouse, and Merriam’s Kangaroo Rat (13% each). In contrast, the most commonly found rodents in Guanajuato were Deer Mouse (39%), and Silky Pocket Mouse (35%) ; whereasEven though vertebrates only represented 10% of total prey items, they accounted for 84% of the total weight consumed, similar to the findings of other authors . Mammal Changes in rodent weight during the second winter regarding the consumption of vertebrates drove the main differences in niche breadth and prey composition among the years studied. These differences coincided with a high annual rainfall that may have resulted in irruptive population events  or causeThese results represent the first systematic effort to investigate the winter diet of Burrowing Owl in prairie dog colonies in northeastern Mexico. The southern Chihuahuan Desert, where the study was conducted, contains the largest expanse of Mexican prairie dog colonies harboring winter populations of Burrowing Owl and other birds with conservation status in North America. Temporal studies that include prey availability in disturbed and undisturbed areas of the southern Chihuahuan Desert would clarify the dynamics of prey use, as well as the of preference for this vulnerable owl species. It would also be instructive to examine the effects of variation in vertebrate weight consumption on the survival of Burrowing Owl during wet and dry years, especially considering climate change scenarios. Another relevant aspect of the temporal framework for diet studies is their relationship with pesticides and indirect exposure to contaminated prey, which is likely, although with limited evidence at the moment .Finally, it is also important to highlight that Llano La Soledad grasslands are key to maintaining healthy populations of the Burrowing Owl as well as other species . The conservation and management of this population depend on the depth of our knowledge of the natural history of this species, including its foraging ecology.10.7717/peerj.13324/supp-1Supplemental Information 1The consumption of the Burrowing Owl diet during the three winter periods. As well as the average weight of the registered preys.Click here for additional data file.10.7717/peerj.13324/supp-2Supplemental Information 2Click here for additional data file.