Auto-Regressive Integrated Moving Average (ARIMA) models are known for their influence and application on time series data. This statistical analysis model uses time series data to depict future trends or values: a key contributor to crime mapping algorithms. However, the models may not function to their true potential when analyzing data with many different patterns. In order to determine the potential of ARIMA models, our research will test the model on irregularities in the data. Our team hypothesizes that the ARIMA model will be able to adapt to the different irregularities in the data that do not correspond to a certain trend or pattern. Using crime theft data and an ARIMA model, we determined the results of the ARIMA model’s forecast and how the accuracy differed on different days with irregularities in crime.

Glioblastoma Multiforme (GBM) is the most malignant brain tumor with the highest fraction of genome alterations (FGA), manifesting poor disease-free status (DFS) and overall survival (OS). We explored The Cancer Genome Atlas (TCGA) and cBioportal public dataset- Firehose legacy GBM to study DNA repair genes Activating Signal Cointegrator 1 Complex Subunit 3 (ASCC3) and Alpha-Ketoglutarate-Dependent Dioxygenase AlkB Homolog 3 (ALKBH3). To test our hypothesis that these genes have correlations with FGA and can better determine prognosis and survival, we sorted the dataset to arrive at 254 patients. Analyzing using RStudio, both ASCC3 and ALKBH3 demonstrated hypomethylation in 82.3% and 61.8% of patients, respectively. Interestingly, low mRNA expression was observed in both these genes. We further conducted correlation tests between both methylation and mRNA expression of these genes with FGA. ASCC3 was found to be negatively correlated, while ALKBH3 was found to be positively correlated, potentially indicating contrasting dysregulation of these two genes. Prognostic analysis showed the following: ASCC3 hypomethylation is significant with DFS and high ASCC3 mRNA expression to be significant with OS, demonstrating ASCC3’s potential as disease prediction marker.

Grammatical gender systems are prevalent across many languages, and when comparing French and English the existence of this system becomes a strong distinction. There have been studies that attribute assigned grammatical gender with the ability to influence conceptualization (attributing gender attributes) of all nouns, thus affecting people's thoughts on a grand scale. We hypothesized that due to the influence of a grammatical gender system, French political discourse would have a large difference between the number of masculine and feminine nouns used. Specifically, we predicted there would be a larger ratio of feminine to masculine nouns in French political discourse than in non-political discourse when compared to English discourse. Through linguistic analysis of gendered nouns in French political writing, we found that there is a clear difference between the number of feminine versus masculine nouns, signaling a preference for a more “effeminate” language.

Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, with early diagnosis and treatment challenges. When any of the genes KRAS, SMAD4, TP53, and BRCA2 are heavily mutated, they correlate with PDAC progression. Cellular stress, partly regulated by the gene SERPINA6, also correlates with PDAC progression. When SERPINA6 is highly expressed, corticosteroid-binding globulin inhibits the effect of the stress hormone cortisol. In this study, the authors explored whether there is an inverse correlation between the expression of SERPINA6 and PDAC-linked genes.

Anticholinergics are used in treating asthma, a chronic inflammation of the airways. These drugs block human M1 and M2 muscarinic acetylcholine receptors, inhibiting bronchoconstriction. However, studies have reported complications of anticholinergic usage, such as exacerbated eosinophil production and worsened urinary retention. Modification of known anticholinergics using bioisosteric replacements to increase efficacy could potentially minimize these complications. The present study focuses on identifying viable analogs of anticholinergics to improve binding energy to the receptors compared to current treatment options. Glycopyrrolate (G), ipratropium (IB), and tiotropium bromide (TB) were chosen as parent drugs of interest, due to the presence of common functional groups within the molecules, specifically esters and alcohols. Docking score analysis via AutoDock Vina was used to evaluate the binding energy between drug analogs and the muscarinic acetylcholine receptors. The final results suggest that G-A3, IB-A3, and TB-A1 are the most viable analogs, as binding energy was improved when compared to the parent drug. G-A4, IB-A4, IB-A5, TB-A3, and TB-A4 are also potential candidates, although there were slight regressions in binding energy to both muscarinic receptors for these analogs. By researching the effects of bioisosteric replacements of current anticholinergics, it is evident that there is a potential to provide asthmatics with more effective treatment options.

Dynamic viscosity is a quantity that describes the magnitude of a fluid’s internal friction or thickness. Traditionally, scientists measure this quantity by either calculating the terminal velocity of a falling sphere or the time a liquid takes to flow through a capillary tube. However, they have yet to conduct much research on finding this quantity through viscous damped simple harmonic motion. The present study hypothesized that the relationship between the dynamic viscosity and the damping coefficient is positively correlated.

The application of machine learning techniques has facilitated the automatic annotation of behavior in video sequences, offering a promising approach for ethological studies by reducing the manual effort required for annotating each video frame. Nevertheless, before solely relying on machine-generated annotations, it is essential to evaluate the accuracy of these annotations to ensure their reliability and applicability. While it is conventionally accepted that there cannot be a perfect annotation, the degree of error associated with machine-generated annotations should be commensurate with the error between different human annotators. We hypothesized that machine learning supervised with adequate human annotations would be able to accurately predict body parts from video sequences. Here, we conducted a comparative analysis of the quality of annotations generated by humans and machines for the body parts of sheep during treadmill walking. For human annotation, two annotators manually labeled six body parts of sheep in 300 frames. To generate machine annotations, we employed the state-of-the-art pose-estimating library, DeepLabCut, which was trained using the frames annotated by human annotators. As expected, the human annotations demonstrated high consistency between annotators. Notably, the machine learning algorithm also generated accurate predictions, with errors comparable to those between humans. We also observed that abnormal annotations with a high error could be revised by introducing Kalman Filtering, which interpolates the trajectory of body parts over the time series, enhancing robustness. Our results suggest that conventional transfer learning methods can generate behavior annotations as accurate as those made by humans, presenting great potential for further research.

In the United States, there are currently 17.8 million affected by atopic dermatitis (AD), commonly known as eczema. It is characterized by itching and skin inflammation. AD patients are at higher risk for infections, depression, cancer, and suicide. Genetics, environment, and stress are some of the causes of the disease. With the rise of personalized medicine and the acceptance of gene-editing technologies, AD-related variations need to be identified for treatment. Genome-wide association studies (GWAS) have associated the Filaggrin (FLG) gene with AD but have not identified specific problematic single nucleotide polymorphisms (SNPs). This research aimed to refine known SNPs of FLG for gene editing technologies to establish a causal link between specific SNPs and the diseases and to target the polymorphisms. The research utilized R and its Bioconductor packages to refine data from the National Center for Biotechnology Information's (NCBI's) Variation Viewer. The algorithm filtered the dataset by coding regions and conserved domains. The algorithm also removed synonymous variations and treated non-synonymous, frameshift, and nonsense separately. The non-synonymous variations were refined and ordered by the BLOSUM62 substitution matrix. Overall, the analysis removed 96.65% of data, which was redundant or not the focus of the research and ordered the remaining relevant data by impact. The code for the project can also be repurposed as a tool for other diseases. The research can help solve GWAS's imprecise identification challenge. This research is the first step in providing the refined databases required for gene-editing treatment.

In this article, the authors use datasets of professional and youth soccer players' movements to map and statistically compare them. Analysis compared movements that led to goals or no-goals and differences between pros and youth.

Van der Woude syndrome is a common birth defect caused by mutations in the gene Irf6. In this project, students used microarray expression analysis from wild-type and Irf6-deficient mice in order to identify gene networks or pathways differentially regulated due to the Irf6 mutation. They found NF-κB pathway to be activated in deficient mice.