Multiple Cross Research Articles

Local error analysis of L1 scheme for time-fractional diffusion equation on a star-shaped pipe network

Abstract The numerical analysis for differential equations on networks has become a significant issue in theory and diverse fields of applications. Nevertheless, solving time-fractional diffusion problem on metric graphs has been less studied so far, as one of the major challenging tasks of this problem is the weak singularity of solution at initial moment. In order to overcome this difficulty, a new L1-finite difference method considering the weak singular solution at initial time is proposed in this paper. Specifically, we utilize this method on temporal graded meshes and spacial uniform meshes, which has a new treatment at the junction node of metric graph by employing Taylor expansion method, Neumann-Kirchhoff and continuity conditions. Over the whole star graph, the optimal error estimate of this fully discrete scheme at each time step is given. Also, the convergence analysis for a discrete scheme that preserves the Neumann-Kirchhoff condition at each time level is demonstrated. Finally, numerical results show the effectiveness of proposed full-discrete scheme, which can be applied to star graphs and even more general graphs with multiple cross points.

Rapid detection of human adenovirus by multiple cross displacement amplification combined with nanoparticle-based biosensor platform

Human adenoviruses (HAdV), particularly serotypes 3 and 7 (HAdV-3 and HAdV-7), are significant respiratory pathogens that contribute to high morbidity rates and severe pneumonia in infants and children. The lack of distinct clinical presentations and effective treatments highlights the urgent need for rapid and reliable diagnostic methods for HAdV-3 and HAdV-7. This study devises a novel detection assay, termed HAdV-MCDA-LFB, which combines isothermal multiple cross displacement amplification (MCDA) with a nanoparticle-based lateral flow biosensor (LFB). Targeting the conserved hexon gene, HAdV-MCDA-LFB demonstrated high sensitivity, detecting low to 10 fg of hexon-containing plasmid per reaction without cross-reaction under the optimized conditions. Moreover, HAdV-MCDA-LFB exhibited comparable diagnostic accuracy to real-time PCR in clinical sample analysis, indicating its practical applicability. The whole procedure, including rapid template preparation (15 min), MCDA reaction (40 min at 67 °C) and result interpretation (<5 min), can be completed within one hour. Together, this rapid turnaround time, coupled with its simplicity and accuracy, makes HAdV-MCDA-LFB a promising point-of-care diagnostic tool for HAdV-3 and HAdV-7, particularly in resource-limited settings.

Visual and fluorescent detection of Moraxella catarrhalis by multiple cross displacement amplification-based assay

Moraxella catarrhalis (M. catarrhalis), once considered as a harmless commensal bacterium, is now recognized as a significant pathogen associated with human mucosal infection. Hence, development of rapid, accurate, and user-friendly detection method is crucial for effective control and management of M. catarrhalis infections. Here, we introduce the M. catarrhalis MCDA-FRT assay, a novel diagnostic approach based on multiple cross displacement amplification (MCDA) for the visual and fluorescent (FRT) detection of M. catarrhalis. This innovative method amplifies the target sequence through MCDA reaction and provides results through the visual observation of a fluorescent signal when exposed to blue light. The generation of the fluorescent signal is facilitated by the cleavage of the restriction endonuclease Nb.BsrDⅠ. The M. catarrhalis MCDA-FRT assay can efficiently detect M. catarrhalis at a temperature of 65 °C in 40 min. It exhibits a remarkable sensitivity of 35 fg of M. catarrhalis genomic DNA, with a specificity of 100%. Furthermore, the assay accurately identified all M. catarrhalis-infected clinical samples tested. In conclusion, the M. catarrhalis MCDA-FRT assay showcases outstanding performance in terms of simplicity, sensitivity, and specificity, which can be used as a promising diagnostic tool for the swift and reliable detection of M. catarrhalis in both field and basic medical settings.

PSIII-6 Multi-sire artificial insemination of beef heifers in the Nebraska Sandhills effect on pregnancy rate compared with single-sire insemination

Abstract The objective of this study was to compare pregnancy outcomes resulting from artificial insemination (AI) with multi-sire semen (MS) compared with single-sire semen (SS) in beef heifers. Another study observed pregnancy and paternity outcomes to sexed MS without a control treatment. In the first study, unrelated ABS Global Angus bulls (n = 3) were selected for AI to 441 (Yr 1) and 391 (Yr 2) Angus crossbred heifers. Heifers were synchronized with the melengestrol acetate – prostaglandin F2α timed-AI protocol. Conventional semen, from each bull, were labeled A, B, or C. Remaining semen was pooled in equal amounts to form conventional MS straws. Heifers were AI using alternating treatment of MS or SS: A, B, or C with one-half of inseminated heifers receiving either MS or SS. The GLIMMIX procedure in SAS 9.4 analyzed responses to treatments using the BIN distribution option and reported as LSMEANS. Contrasts and estimates analyzed responses between treatments. Ultrasound determined SS pregnancy rate averaged 60% (SS-A = 64%, SS-B = 58%, SS-C = 58%) and MS average 62% (P = 0.63). Paternity was determined in 57 MS calves and confirmed in 49 SS calves. In Yr 1, Bull A sired 7%, bull B sired 56%, and bull C sired 37%. Although bull A was much less successful than bull B or C, the values are not significantly different due to the small sample size (P = 0.11 and P = 0.24, respectively). Few conclusions can be made from the ongoing study, but it is hypothesized MS may improve consistency of pregnancy rate by equilibrating semen quality in herds subjected to timed AI. In the observational study, multiple 5-way cross bulls born from the ranch herd were selected for AI of 951 ± 41 heifers for 3 yr. Heifers were synchronized using the previously mentioned protocol with split-time AI. Unequal amounts of semen were collected, pooled, and sexed to favor heifer progeny at an expected ratio of 9:1. Semen from 2 to 4 random bulls were pooled making 3 to 4 MS groups each year. Heifers exhibiting estrus were administered sexed MS, but there was no control treatment. The observed ultrasound determined average pregnancy rate of heifers administered sexed MS was 65% (Yr 1), 75% (Yr 2), and 72% (Yr 3). Previous literature suggests average pregnancy rate to AI with sexed SS is 53%. Paternity testing in Yr 2 found semen contribution mirrored paternity ratios within some groups but the sire group with 2 bulls found one sired 15% and the other sired 85%. This study exemplified a viable method for AI that requires further research. Further research into semen quality of a single sire, and pooled semen could lead to optimization of fertilization among individuals within a herd.

A one-pot, one-step CRISPR platform (CRISPR-one) for nucleic acid detection: Application for the detection of Mycoplasma pneumoniae

The conventional CRISPR-mediated detection technologies typically involve two separate steps: target pre-amplification and CRISPR-mediated signal detection. This multi-step procedure is not only cumbersome but also prone to cross-contamination. Here, we propose a new CRISPR-mediated nucleic acid detection methodology, termed CRISPR-one (CRISPR-mediated testing in one-tube, one-pot). At a constant temperature, CRISPR-one carries out both multiple cross displacement amplification (MCDA) for target pre-amplification and CRISPR/AapCas12b-based cleavage for signal detection in a single reaction pot. The novel CRISPR-one technique is employed for detecting Mycoplasma pneumoniae (M. pneumoniae), a causative agent for community-acquired pneumonia in children. M. pneumoniae CRISPR-one targets the CARDS (community-acquired respiratory distress syndrome toxin) gene and performs at 60 ºC for 50 min with only basic instrumentation required. The results can be easily visualized through a real-time fluorescence instrument, with the entire process taking less than an hour. The lowest detection limit of M. pneumoniae CRISPR-one was 50 fg (∼55 copies) of genomic DNA per reaction, and no cross-reactivity with non-M. pneumoniae templates was observed. In clinical applications, the CRISPR-one assay demonstrated a high level of agreement with both the microfluidic chip method (Kappa value: 0.95) and real-time PCR (Kappa value: 0.93) when analyzing 135 oropharynx swab samples. There is no statistical difference in detection efficiency between our assay and other methods. Together, the CRISPR-one developed here offers a rapid, convenient, highly sensitive, and specific approach for pathogen detection (e.g., M. pneumoniae), serving as an invaluable tool for clinical diagnosis and epidemiologic surveillance, particularly in resource-limited laboratories.

FC-StackGNB: A novel machine learning modeling framework for forest fire risk prediction combining feature crosses and model fusion algorithm

Forest fire risk prediction is a crucial link in maintaining forest ecological security. Machine learning, due to its powerful non-linear modeling capabilities, has been widely applied in forest fire risk prediction research. However, existing studies often focus on the direct information provided by multiple environmental factor features when constructing the feature space, while overlooking the deeper information conveyed by feature cross-correlations. Additionally, fire risk prediction predominantly relies on single-model forecasting, exhibiting slightly insufficient generalization and stability in models. Model fusion algorithms (MFA) can combine the advantages of multiple models to compensate for this limitation. In this study, a machine learning framework, FC-StackGNB, combining feature crosses (FC) and model fusion, is proposed. This framework employs the FC method to analyze the temporal trends of various environmental factors influencing fire occurrence, constructing multiple seasonal cross features (SCFs) capable of effectively capturing the non-linear relationship between environmental factors and time. Moreover, the framework develops a Gaussian Naive Bayes (GNB) optimized stacking MFA to fully leverage the strengths of different ML algorithms. Results demonstrate that the introduction of SCFs effectively enhances the prediction performance of six machine learning models, with the mean values of five evaluation metrics (Accuracy, Precision, Recall, F1-score, and ROC_AUC) increasing by 1.58% to 6.30%. The fusion model constructed based on the StackGNB algorithm can effectively handle the multicollinearity issue of features, exhibiting significantly better prediction performance than single models, particularly in improving the Recall metric (increasing by around 3% and 5% compared to the top two ranked single models respectively), which signifies the model’s ability to predict positive samples (i.e., high-risk fire areas). The proposed modeling framework effectively enhances the robustness and prediction performance of the models, offering new modeling insights for subsequent research. This study holds significant importance for enhancing the level of forest fire risk warning.

Multivariate analysis and image-based phenotyping of cayenne fruit traits in selection and diversity mapping of multiple F1 cross lines

The phenomenon of fluctuating chili prices can be resolved in stages, one of which is through multiple crosses. However, this cross requires precise methods in the evaluation and selection process, especially regarding fruit characteristics. Image-based phenotyping 4.0 approaches can increase the potential precision of such evaluation genotypes, especially when this approach is combined with multivariate analysis. Therefore, both methods are needed to evaluate and select these cayenne multiple crosses. This research aims to identify the effectiveness of multivariate analysis and image-based explanatory characteristics of fruit phenotypes and to select multiple crosses that can continue to the F2 generation. The research was designed with a randomized complete block design of ten F1 multiple cross-genotypes and four check varieties. Each genotype was repeated three times, so there were 42 experimental units. Based on the results, multivariate was considered adequate in determining image explanatory characters based on fruit phenotype and genotype mapping of the population diversity of multiple crosses of cayenne pepper. The characteristics of fruit height, fruit area, and fruit Intden are image-based explanatory characters that can map the completeness of cayenne pepper fruit between multiple crosses well. This indicates that image-based phenotyping and multivariate analysis can provide more detailed image information of the potential of cayenne fruit from multiple crosses than just based on fruit weight. Therefore, both approaches are recommended for analyzing cayenne paper fruit potential, especially for multiple crosses. In addition, three crosses (MC4, MC8, and MC9) are optimal for the next generation to be recommended and continued.

Neuropathological hallmarks in the post-mortem retina of neurodegenerative diseases

The retina is increasingly recognised as a potential source of biomarkers for neurodegenerative diseases. Hallmark protein aggregates in the retinal neuronal tissue could be imaged through light non-invasively. Post-mortem studies have already shown the presence of specific hallmark proteins in Alzheimer’s disease, primary tauopathies, synucleinopathies and frontotemporal lobar degeneration. This study aims to assess proteinopathy in a post-mortem cohort with different neurodegenerative diseases and assess the presence of the primary pathology in the retina. Post-mortem eyes were collected in collaboration with the Netherlands Brain Bank from donors with Alzheimer’s disease (n = 17), primary tauopathies (n = 8), synucleinopathies (n = 27), frontotemporal lobar degeneration (n = 8), mixed pathology (n = 11), other neurodegenerative diseases (n = 6), and cognitively normal controls (n = 25). Multiple cross sections of the retina and optic nerve tissue were immunostained using antibodies against pTau Ser202/Thr205 (AT8), amyloid-beta (4G8), alpha-synuclein (LB509), pTDP-43 Ser409/410 and p62-lck ligand (p62) and were assessed for the presence of aggregates and inclusions. pTau pathology was observed as a diffuse signal in Alzheimer’s disease, primary tauopathies and controls with Alzheimer’s disease neuropathological changes. Amyloid-beta was observed in the vessel wall and as cytoplasmic granular deposits in all groups. Alpha-synuclein pathology was observed as Lewy neurites in the retina in synucleinopathies associated with Lewy pathology and as oligodendroglial cytoplasmic inclusions in the optic nerve in multiple system atrophy. Anti-pTDP-43 generally showed typical neuronal cytoplasmic inclusion bodies in cases with frontotemporal lobar degeneration with TDP-43 and also in cases with later stages of limbic-associated TDP-43 encephalopathy. P62 showed inclusion bodies similar to those seen with anti-pTDP-43. Furthermore, pTau and alpha-synuclein pathology were significantly associated with increasing Braak stages for neurofibrillary tangles and Lewy bodies, respectively. Mixed pathology cases in this cohort consisted of cases (n = 6) with high Braak LB stages (> 4) and low or moderate AD pathology, high AD pathology (n = 1, Braak NFT 6, Thal phase 5) with moderate LB pathology, or a combination of low/moderate scores for different pathology scores in the brain (n = 4). There were no cases with advanced co-pathologies. In seven cases with Braak LB ≥ 4, LB pathology was observed in the retina, while tau pathology in the retina in the mixed pathology group (n = 11) could not be observed. From this study, we conclude that the retina reflects the presence of the major hallmark proteins associated with neurodegenerative diseases. Although low or moderate levels of copathology were found in the brains of most cases, the retina primarily manifested protein aggregates associated with the main neurodegenerative disease. These findings indicate that with appropriate retinal imaging techniques, retinal biomarkers have the potential to become highly accurate indicators for diagnosing the major neurodegenerative diseases of the brain.

Trivariate copula functions for constructing a comprehensive atmosphere-land surface-hydrology drought index: A case study in the Yellow River basin

The current univariate and multivariate drought indices cannot fully reflect the multiple processes of meteorological-hydrological-agricultural drought. So it is important to construct a comprehensive drought index for drought monitoring in a changing environment from the perspective of the whole process of the natural water cycle “atmosphere-land surface-hydrology”. In this study, based on the standardized indices (SCI) framework and trivariate Copula functions, a multi-scale comprehensive drought index (SPERSI) integrating rainfall, temperature, runoff and soil moisture is proposed, which can synthetically reflect meteorological, hydrological and agricultural droughts. The spatial and temporal variations of drought in the Yellow River Basin (YRB) from 1961 to 2019 were investigated, and then the links between SPERSI and some remotely correlated factors were revealed by using the multiple cross wavelet transform technique. Results show that: (1) the SPERSI constructed by fusing rainfall-temperature-runoff-soil moisture information can sensitively and effectively capture the onset, persistence and termination of drought; (2) the SPERSI exhibits good performance in detecting integrated drought events in the YRB with probability of detection (POD) > 0.75, false alarm rate (FAR) < 0.04 and critical success index (CSI) > 0.73; (3) From 1961 to 2019, drought in different time scales of above Longyangxia (AL) and Longyangxia to Lanzhou (LL) zones in the YRB both show a slight mitigation trend, while the drought in remaining subzones show a trend of intensification; (4) Multiple cross-wavelet results indicate that AO is the dominant factor of drought in the Yellow River Basin, followed by ENSO. In general, SPERSI results are reliable and can provide basis for drought decision-making.

Estimating the dynamics of greenhouse gas emission during black soldier fly larvae growth under controlled environmental conditions

Edible insects are considered a sustainable alternative protein source. Several studies have indicated that greenhouse gas (GHG) emissions from insects are generally low. These data were usually recorded in closed static chambers, which did not reflect the actual rearing conditions. On the other hand, open respiration chambers need high amounts of substrate and insects, making the analyses time consuming and expensive. In the present study, a novel small-scale open gas exchange recording system for evaluation of GHG emissions from insects is presented. The device consists of four independent respiration chambers, each one equipped with a pump for supplying air, a flow meter and a septum for sampling the flowing air. A validation experiment was carried out using black soldier fly larvae as model organisms and moistened chicken feed (dry matter 40%) as rearing substrate. Three independent runs were conducted, each with two replicates, using 200 neonates larvae and 300 g of substrate. Moistened chicken feed (300 g) without larvae was used as control. Throughout the 12 days rearing period, gas sampling occurred every 12 h. Data interpolation considering a linear tendency between two successive sampling points was applied and overall amounts of gases were quantified as area under the curve. Irrespective of the run, CO2 emissions in the insect-substrate systems increased during the initial 7 d of experiment, consistently followed by a rapid decrease. Multiple cross correlation analysis showed high reproducibility (r ≥ 0.9), while significant differences in the CO2 emissions between insects-substrate system (range 7.76–11.88 g g−1 dry insects) and sole substrate (range 0.20–0.33 g g−1 initial dry substrate) were observed. Very limited N2O (≤1.13 mg g−1 dry insects) and CH4 (≤1.33 mg g−1 dry insects) emissions were detected in this experiment, indicating that the performed black soldier fly larvae rearing did not result in significant production of these GHGs. Overall, the experiment showed that the novel device may be particularly suited for quick and reliable estimation of GHG emissions from edible insects.

The Next Generation Deep Extragalactic Exploratory Public Near-infrared Slitless Survey Epoch 1 (NGDEEP-NISS1): Extragalactic Star-formation and Active Galactic Nuclei at 0.5 < z < 3.6

The Next Generation Deep Extragalactic Exploratory Public (NGDEEP) survey program was designed specifically to include Near Infrared Slitless Spectroscopic observations (NGDEEP-NISS) to detect multiple emission lines in as many galaxies as possible and across a wide redshift range using the Near Infrared Imager and Slitless Spectrograph. We present early results obtained from the first set of observations (Epoch 1, 50% of the allocated orbits) of this program (NGDEEP-NISS1). Using a set of independently developed calibration files designed to deal with a complex combination of overlapping spectra, multiple position angles, and multiple cross filters and grisms, in conjunction with a robust and proven algorithm for quantifying contamination from overlapping dispersed spectra, NGDEEP-NISS1 has achieved a 3σ sensitivity limit of 2 × 10−18 erg s−1 cm−2. We demonstrate the power of deep wide field slitless spectroscopy (WFSS) to characterize the star formation rates, and metallicity ([O iii]/Hβ), and dust content, of galaxies at 1 < z < 3.5. The latter showing intriguing initial results on the applicability and assumptions made regarding the use of Case B recombination. Further, we identify the presence of active galactic nuclei and infer the mass of their supermassive black holes using broadened restframe Mg ii and Hβ emission lines. The spectroscopic results are then compared with the physical properties of galaxies extrapolated from fitting spectral energy distribution models to photometry alone. The results clearly demonstrate the unique power and efficiency of WFSS at near-infrared wavelengths over other methods to determine the properties of galaxies across a broad range of redshifts.

Cost-effectiveness of patch testing allergens within the same group: A computational approach to optimize formaldehyde-related allergen selection

BackgroundPatch testing to multiple cross reactive allergens for allergic contact dermatitis (ACD) may not be necessary due to copositivity. ObjectiveWe evaluated the formaldehyde group allergens to determine the optimal, most cost-effective allergens to test. MethodsA retrospective analysis of Mayo Clinic (1997-2022) examined the well-established copositive formaldehyde group: Formaldehyde, Quaternium 15, Hexahydro-1,3,5-tris(2-hydroxyethyl)triazine, Diazolidinyl urea, Imidazolidinyl urea, Toluenesulphonamide formaldehyde resin, DMDM hydantoin, and Ethyleneurea melamine formaldehyde mix. Patch Optimization Platform (POP) identified which single formaldehyde-related allergen optimally captures patients with clinically relevant ACD. Next, POP determined the optimal additional 1, 2, 3, etc. allergens. Cost per patch test was $5.19 (Medicare 2022). Results9832 patients were tested to all listed allergens, with 830 having positive patch tests. POP determined that Quaternium 15 alone captures 53% of patients with ACD to the formaldehyde group; adding the optimal second allergen (Formaldehyde 1%) captures 78%; the optimal five top allergens capture over 94% of patients. The incremental cost-per-additional-diagnosis increased up to 44-fold as the number of allergens tested increased. LimitationsData is from a single institution, and the cost-per-test was fixed to Medicare Part B in 2022. ConclusionsFor diagnosing ACD, we recommend considering an optimized allergen selection algorithm.

A fused convolutional transformer for voltammetric electronic tongue analysis tasks

Electronic tongue and artificial intelligence have been evaluated as new rapid analysis technologies in complex solution research. The use of nanomaterials to modify the screen-printed carbon electrode (SPCE) increased the specific surface area and electrocatalytic active sites of the electrode. The voltammetric electronic tongue utilizes multiple cross sensitive sensors to describe the overall characteristics of complex samples, and the complementary information between sensors provides rich and comprehensive descriptions for analyzing various components. The use of electronic tongue for rapid analysis of water quality has gradually become a trend, but the overlapping phenomenon of peaks formed by the similar redox potential of each component in water and the interference of the substrate makes it difficult to obtain effective information of each component in water quality. Here, we propose a Fused Convolutional Transformer model (FCvT) that removes the limitation of overlapping peaks by fusing local features and global complementary features to quantify components in complex solutions. The performance of FCvT was evaluated by testing synthetic water samples consisting of four standardized substance solutions and compared with partial least squares (PLS), support vector regression (SVR), artificial neural networks (ANN), and convolutional neural networks (CNN). The results show that the FCvT obtains the lowest mean absolute percentage error in the set of independent tests.The average quantization error of FCvT is reduced by 34.4 % relative to other methods.

Knowledge, attitudes, and current practices toward lung cancer palliative care management in China: A national survey.

11143 Background: To demonstrate the status and differences in knowledge, attitudes, and practices (KAP) of lung cancer palliative care (LCPC) management, and to measure patient controlled analgesia (PCA) in cancer pain management in of China. Methods: A questionnaire on LCPC management was used in this study, which involved a total of 2093 participants from 706 hospitals in China. Seven major components make up the questionnaire, including chi-square tests or Fisher exact probabilities to measure the differences in KAP between hospitals grades. Comparing distributions of ordered data between groups was done using the Kruskal-Wallis H test or the Mann-Whitney U test. Multiple choice questions use multiple response cross analysis. Correlation was evaluated by the Spearman correlation coefficient. Results: 84.2% participants believed that anti-tumor therapy is equally important as palliative care. The satisfaction rate of participants from grade 3 hospitals, which was significantly higher than that of grade 2 and grade 1 hospitals (χ²=27.402, P=0.002). The most common symptoms requiring LCPC was pain. The major barriers toward to LCPC were “Patients and families are concerned about the safety of long-term use of palliative care related drugs”. The most common reasons for the use of PCA treatment were 31.1% participants thought “Patients with systemic application of large doses of opioids or adverse reactions to opioids that cannot be tolerated”. The top three barriers toward PCA treatment of cancer pain were (i) worry about adverse reactions of drug overdose, (ii) worry about opioid addiction, and (iii) increase of patients' economic burden. In the past 24 months, 33.9% of the participants had not participated in online or offline training related to palliative care of lung cancer. Conclusions: Chinese healthcare workers are in need of training for lung cancer palliative care and, in particular, for controlling cancer pain symptoms.

Development and clinical evaluation of a real-time multiple cross displacement amplification assay for rapid and sensitive detection of Mycobacterium tuberculosis

Molecular techniques of nucleic acid testing recommended by the World Health Organization (WHO) for the Mycobacterium tuberculosis (MTB) detection were considered to have the potential access to the accurate tuberculosis (TB) notifications. In this study, a new method, which coupled real-time (rt) fluorescence technique with multiple cross displacement amplification (MCDA), was developed for the rapid, sensitive and specific detection of MTB (termed MTB-rt-MCDA). According to the principle of the rt-MCDA test, a set of ten primers were designed for the MCDA reaction, of which one was engineered with a restrictive endonuclease recognition site, a fluorophore and a quencher for achieving the real-time fluorescence detection. MTB-rt-MCDA test was conducted under the optimized conditions (67 °C, 40 min) on the real-time fluorescence platform. The MTB-rt-MCDA assay accurately identified the MTB strains with no cross reaction with other bacteria. The lowest detectable genomic DNA concentration of the MTB-rt-MCDA assay was 25 fg/μl. We employed the genomic DNA templates extracted from sputum of clinical cases for validating the practical applicability of this assay, and the detection power of the MTB-rt-MCDA assay was comparable to that of the Xpert method and MCDA-based biosensor detection and superior to smear microscope method. The complete process of the MTB-rt-MCDA assay, including rapid extraction of DNA and rt-MCDA test, takes less than 1 h. In conclusion, the presented MTB-rt-MCDA assay provided an effective and simple option for the rapid screening of MTB infection.

Silicosis and silica-induced autoimmunity in multiple Collaborative Cross strains.

Abstract Background: The connection between pulmonary silica dust exposure and the development of systemic autoimmunity is well established. However, the extent to which genetic differences influence the severity of silica-induced autoimmunity remains unclear. Objective: Investigate the role of silica exposure on disease biomarkers (cytokines/chemokines and antinuclear antibodies (ANA)) and systemic autoimmune disease in genetically diverse Collaborative Cross recombinant inbred (CC RI) mice. Methods: Baseline serum ANA and chemokines/cytokines were assessed from 61 CC RI strains. Six CC RI strains, chosen to represent different baseline immune phenotypes, were exposed to 5 mg crystalline silica or PBS and examined 12 weeks later for serum ANA, bronchoalveolar lavage fluid cell counts, tracheobronchial lymph node and spleen weights, and histological assessment of silicosis and lupus nephritis. Results: All strains developed silicosis; however, two strains with low baseline immune activity did not develop silica-induced autoimmunity. In contrast, strains with higher baseline immune activity developed elevated ANA levels with strain variation in ANA types. One strain developed lupus nephritis, with significantly greater severity compared to controls. Conclusion: Baseline immune activity significantly influences the development of silica-induced autoimmunity and lung inflammation; however, genetics may be mediating silicosis and systemic autoimmunity independently.

Using positional tracking to improve abdominal ultrasound machine learning classification

Diagnostic abdominal ultrasound screening and monitoring protocols are based around gathering a set of standard cross sectional images that ensure the coverage of relevant anatomical structures during the collection procedure. This allows clinicians to make diagnostic decisions with the best picture available from that modality. Currently, there is very little assistance provided to sonographers to ensure adherence to collection protocols, with previous studies suggesting that traditional image only machine learning classification can provide only limited assistance in supporting this task, for example it can be difficult to differentiate between multiple liver cross sections or those of the left and right kidney from image post collection. In this proof of concept, positional tracking information was added to the image input of a neural network to provide the additional context required to recognize six otherwise difficult to identify edge cases. In this paper optical and sensor based infrared tracking (IR) was used to track the position of an ultrasound probe during the collection of clinical cross sections on an abdominal phantom. Convolutional neural networks were then trained using both image-only and image with positional data, the classification accuracy results were then compared. The addition of positional information significantly improved average classification results from ∼90% for image-only to 95% for optical IR position tracking and 93% for Sensor-based IR in common abdominal cross sections. While there is further work to be done, the addition of low-cost positional tracking to machine learning ultrasound classification will allow for significantly increased accuracy for identifying important diagnostic cross sections, with the potential to not only provide validation of adherence to protocol but also could provide navigation prompts to assist in user training and in ensuring adherence in capturing cross sections in future.

THE RELATIONSHIP BETWEEN PROCRASTINATION AND SKEPTICISM OF FEB UNS STUDENTS ON INTEREST IN INVESTING

&lt;p&gt;The habits of students who prefer to procrastinate and doubt something, also influence the attitude in starting an action, one of which is the interest in investing. Student interest in investing is relatively low although the majority of majors in the faculties of economics and business will get knowledge related to the capital market. The purpose of this study was to determine whether there is a relationship between the character of students in procrastination (procrastination) and skepticism (hesitation) of the students of the Faculty of Economics and Business at Sebelas Maret University on the interest in investing in the capital market. This research is a quantitative study with a sample of research using the Stratified Sampling technique, which is based on the level of levels that exist among FEB UNS students (Diploma-3, Strata-1, Masters, and Doctor). The instrument used was an online questionnaire. Data is processed through the SPSS program using multiple regression analysis and cross tabulation. The results showed that procrastination has a negative and significant effect on the interest in investing in students. Meanwhile, skepticism has a positive and significant effect on the interest in investing in students.&lt;/p&gt;

Long-term responses of maar lakes water level to climate and groundwater variability in central Mexico

Besides water level (WL) variability, several lakes worldwide have shown water level decline trends and total desiccation. In maar lakes, there is paleoenvironmental evidence of water level responses to climate and groundwater inflow changes; however, historical studies are scarce. Understanding the relative contributions of natural variability and anthropogenic impacts to the decline of lakes' water level is fundamental to preserving these ecosystems. In this study, we developed a conceptual baseline of the response of tropical maar lakes to climate and groundwater variability at different time scales in near natural conditions. The four analyzed maar lakes are in a semiarid, endorheic basin in central Mexico and the study time frame (1959 to 1992) includes a historical period before direct human impact. We analyzed the effects of meteorological records, large-scale climate phenomena indices (ENSO, PDO, and AMO), and regional groundwater levels on WL datasets using multiple linear regression models, trend analysis and cross correlations. WL responded to meteorological seasonality and climate variability indices. All lakes lost more water to evaporation than they gained from precipitation, which partially explains long-term WL declines. Regional groundwater levels decreased, suggesting a reduction of groundwater inflow to the lakes, which contributed to WL declines. The latter may reflect a lagged effect of long-term recharge decrease, probably due to long-term climate change in the region since the formation of the lakes evidenced in previous studies. The results add to evidence of water level declines caused by groundwater-mediated natural variability, warning about how this process is being exacerbated by anthropic activities under the context of global change.

Abstract A015: Predicting efficacious drugs and drug-biomarker relationships for targeted glioblastoma treatment

Abstract Glioblastoma multiforme (GBM) is the deadliest, most heterogeneous, and common brain cancer in adults. Despite major advancements in neurosurgery as well as chemotherapy and radiotherapy techniques, overall prognosis has changed little over the decades. Not only is there an urgent need to identify efficacious therapeutics but there is also a great need to pair these therapeutics with biomarkers that can help tailor treatment to the right patient populations given the heterogeneous nature of the disease. Here, we implement several machine learning and statistical based pipelines for drug and biomarker discovery, leveraging previous work building patient drug response models using high-throughput cell line drug screening data as well as Bayesian network structure learning. Through these discovery pipelines, we identified multiple agents of interest for GBM, including MEK inhibitors (MEKis). MEKis were consistently predicted to be effective against GBM in multiple cross platform (microarray and RNA-sequencing) patient and patient derived xenograft (PDX) datasets. Additionally, they were experimentally shown to be more efficacious in tumor samples than standard of care agents (Temozolomide and Carmustine). We also predicted PHGDH gene expression levels to be causally associated with MEKi efficacy, which increased tumor sensitivity to a MEKi (Trametinib) during experimental testing in GBM cell lines with knockdown. These findings support our drug and biomarker discovery pipelines as an effective strategy to unite multiple cross platform and cross species datasets to help achieve GBM precision medicine. They also demonstrate that patient PHGDH levels can help inform Trametinib response in the fight against GBM. Citation Format: Danielle Maeser, Robert Gruener, Robert Galvin, Stephanie Huang, Tomo Koga, Florina Grigore. Predicting efficacious drugs and drug-biomarker relationships for targeted glioblastoma treatment [abstract]. In: Proceedings of the AACR Special Conference on Brain Cancer; 2023 Oct 19-22; Minneapolis, Minnesota. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_1):Abstract nr A015.