Texas Research Institute

Abstract Bovine respiratory disease (BRD), enteric emissions, and economic efficiency represent three critical sustainability challenges in global beef cattle production. BRD, a polymicrobial and multifactorial disease, incurs annual losses of $2 billion in the U.S. cattle industry and is the primary driver of antibiotic use across food animal production. Despite livestock emissions accounting for only 4% of total U.S. greenhouse gas emissions, beef cattle contribute 55% of these emissions, totaling 300 million tons annually. Additionally, optimizing economic outcomes across the beef value chain is essential for the industry’s sustainability. Our Artificial Intelligence for sustaianble livestock systems laboratory is developing an Hybrid-Intelligent Mechanistic Modeling-based Decision Support System (HIMM-DSS) designed to address these challenges through real-time interventions within cattle herds. Leveraging the extensive precision livestock farming infrastructure of Texas A&M System, including video surveillance and biosensor-based data collection, the HIMM-DSS integrates multiple PLF sensors with both mechanistic and artificial intelligence-based modeling. For BRD mitigation, traditional methods of monitoring depression, appetite, respiration, and temperature is being enhanced using PLF technologies such as activity sensors, depth cameras, lung-ultrasonography, and infrared thermography. Studies across the U.S. have demonstrated that boosted decision trees applied to sensor data provide a reliable positive predictive value for disease detection. The HIMM-DSS facilitates early BRD prediction, reducing its prevalence and the need for antibiotics. For emission reduction, the HIMM-DSS employs advanced sensors like Growsafe, greenfeed systems, and trutest based body weight prediction for real-time gas-flux versus animal performance analysis. By combining mechanistic modeling, such as the cattle value discovery system, with sensor data, we are developing systems that can accurately quantify greenhouse gas emissions and animal performance. This system supports the use of methane-mitigating feed additives, providing real-time evidence for progress towards carbon-neutrality benchmarks in the cattle industry. Additionally, we incorporate an economic analysis of the beef value chain, encompassing cow-calf, stocker, and feedlot economics. By applying machine learning techniques, we develop decision tools that analyze and optimize economic outcomes across these segments. These tools enable stakeholders to make informed decisions, enhancing profitability and sustainability throughout the beef production system. The HIMM-DSS represents a significant advancement in sustainable beef cattle production, offering practical solutions to the industry’s most pressing challenges in disease management, emissions reduction, and economic efficiency.

ABSTRACT The work presented in this article focused on the development of a pseudo-3D computational approach for visualizing computational ultrasonic inspection models for thin composite laminates with complex geometry. The approach creates a 3D dataset by merging several 2D models along different cross-sectional slices of the material under inspection. The approach was applied to a 2.6 mm thick carbon fiber epoxy laminate with an annulus-shaped artificial defect concentric with a 4 mm diameter through hole, shadowed by a 100° countersink. Investigations were performed using water immersion ultrasonic pulse-echo and single-sided pitch-catch setups operating at a 5 MHz frequency. Time-of-flight comparisons between computational and experimental A-scans validated the computational results, demonstrating a calibration error of 4%. Defect sizes measured in the C-scans of the computational model matched the actual outer diameter and width of the annulus, with a difference of 18% and 6%, respectively. Findings within the experimental dataset were similar, with the largest difference associated with the defect width being off by 0.088 mm. The pseudo-3D approach revealed that a normal incidence water immersion setup was ineffective in detecting defects from the countersunk geometry, while an oblique incidence inspection at 8.4375° was successful in capturing the defect with reasonable accuracy.

Abstract Artificial intelligence (AI) has grown widely into diverse areas of research over the last decade. Publications distributed over the years disclose that biochemistry and analytical chemistry are incorporating AI to the utmost level. Enormous complex data sets are produced by analytical instruments comprising a treasure of information which is very helpful to analytical chemists in characterization. Despite this, we lack capabilities in data analysis and algorithms, which restricts our aptitude to discover and employ this data entirely. Machine learning and AI are being instigated to deal with this current challenge by accelerating several other applications in analytical chemistry. A critical valuation of illustrative reports amalgamating AI with analytical chemistry, spectroscopies, and several sensors has been discussed widely. This comprehensive review evaluates the AI advancement in data interpretation, the assistance of AI in analyte identification and quantification in proteomics and metabolomics, AI databases for primary and secondary metabolites information, regulatory and ethical guidelines in analytical chemistry incorporating AI, machine learning applications in drug discovery, agriculture, cosmetic, and food and separation techniques. In summary, this current review puts forward a thorough examination concerning the advancements of AI in different domains of chemistry and strives to facilitate comprehension of its forthcoming trajectories.

Frequency Selective Surfaces (FSSs) are periodic arrays of conductive elements or apertures that reflect and/or transmit incident electromagnetic energy. Their response depends on parameters such as element shape, unit cell dimensions, dielectric properties, and the local environment, making them suitable for structural health monitoring (SHM) applications. This paper presents a dual-parameter FSS-based sensor design capable of measuring small-scale uni-directional longitudinal strain (0%–0.5%) and temperature (230C–2230C). The sensor integrates two-unit cells: a patch-based cell on a thin substrate for strain sensing, offering enhanced strain transfer and superior sensitivity ( 16–18 MHz/0.1%), and a loop-based cell with a temperature-sensitive dielectric for temperature measurements, achieving a sensitivity of 0.54 MHz/0C. The dual-measurand capability is achieved by designing the sensor with two distinct resonant frequencies, each corresponding to a specific parameter. Simulation and measurement results demonstrate that the proposed sensor achieves greater strain sensitivity as compared to existing FSS-based strain sensors while maintaining temperature sensitivity on par with existing FSS temperature sensors. The study also characterizes thermal expansion-induced errors through simulation and proposes a compensation approach that successfully improves sensitivity. Overall, this work demonstrates the potential of FSS-based sensors as compact, multi-measurand solutions for SHM applications, offering high sensitivity and reliability with minimal cross-sensitivity effects.

BackgroundPrevious epidemiological studies of the associations between polyunsaturated fatty acids (PUFAs) and cancer incidence have been inconsistent. We investigated the associations of plasma omega-3 and omega-6 PUFAs with the incidence of overall and 19 site-specific cancers in a large prospective cohort.Methods253,138 eligible UK Biobank participants were included in our study. With a mean follow-up of 12.9 years, 29,838 participants were diagnosed with cancer. The plasma levels of omega-3 and omega-6 PUFAs were expressed as percentages of total fatty acids (omega-3% and omega-6%).ResultsIn our main models, both omega-6% and omega-3% were inversely associated with overall cancer incidence (HR per SD = 0.98, 95% CI = 0.96–0.99; HR per SD = 0.99, 95% CI = 0.97–1.00; respectively). Of the 19 site-specific cancers available, 14 were associated with omega-6% and five with omega-3%, all indicating inverse associations, with the exception that prostate cancer was positively associated with omega-3% (HR per SD = 1.03, 95% CI = 1.01 – 1.05).ConclusionsOur population-based cohort study in UK Biobank indicates small inverse associations of plasma omega-6 and omega-3 PUFAs with the incidence of overall and most site-specific cancers, although there are notable exceptions, such as prostate cancer.

Distal hand ischemia syndrome (DHIS) is a well reported adverse outcome in patients with upper arm AV access. 25%-40% of these patients have been reported to be due to primary arterial disease complicated with significant arterial calcification. The effectiveness of revascularization of the distal arterial circulation on symptom resolution has not been reported yet. Retrospective single center analysis of patients evaluated for hand/forearm pain in patients with upper arm AV access who had arterial revascularization between 01/2016 and 12/2020 were included for the analysis. Fifty-one patients met inclusion criteria. Stenotic lesions greater than 70% in the subclavian, axillary, brachial, radial, or ulnar artery were treated with balloon angioplasty. Institutional approval was obtained to review charts. Successful revascularization, improvement in pain in 48 h, 1 month, and 3 months. Seventy six percent of patients had an upper arm Arteriovenous Fistula (AVF) and 24% patients had an upper arm Arteriovenous Graft (AVG). Mean access flow was 1210 (556) ml/min. 55% of patients had radial or ulnar arterial stenosis, 45% had brachial/axillary or subclavian artery stenosis. 45% patients had lesions in both radial and ulnar arteries, 88% of patients were successfully revascularized. 76% (18) of patients had improvement in symptoms within 48 h and 68% remained symptom free in 3 months. Mean DHIS stage was 3.1 before intervention and improved to 1.1 post intervention (p < 0.001). Patient satisfaction with their AV access improved from 34% to 72% (p < 0.01). Multiple regression analysis did not reveal statistically significant correlations between time on dialysis vintage and other chronic medical conditions on post procedure symptom improvement. DHIS with occlusive arterial disease can be successfully revascularized to improve symptoms. Complete evaluation of the inflow arterial segment and optimal endovascular revascularization could decrease the need for access revision procedures or access abandonment.

Social avoidance has been associated with more persistent social anxiety disorder (SAD) symptoms and low testosterone levels in individuals with SAD. We tested whether pre-treatment avoidance tendencies moderate the efficacy of testosterone-augmented exposure therapy. Fifty-five females with SAD received two exposure sessions during which fear levels were assessed. Session 1 was augmented with testosterone (0.50 mg) or placebo. Avoidance tendencies and symptom severity were assessed pre- and post-exposure. Participants showed stronger avoidance for social versus non-social stimuli and this tendency remained stable over time. Stronger pretreatment avoidance tendencies were associated with larger fear reduction in the testosterone but not the placebo condition. This effect did not transfer to the second non-enhanced session or symptom severity. The findings support the hypothesis that individuals suffering from SAD with relatively stronger pretreatment avoidance tendencies benefit more from testosterone-augmentation, pointing to a potential behavioral marker for testosterone enhancement of exposure therapy.