Miracle-dz/UniFAR-FTUs huggingface dataset:
Acoustic monitoring yields informative bat population density estimates

Abstract Bat population estimates are typically made during winter, although this is only feasible for bats that aggregate in hibernacula. While it is essential to measure summer bat population sizes for management, we lack a reliable method. Acoustic surveys should be less expensive and more efficient than capture surveys, and acoustic activity data are already used as indices of population size. Although we currently cannot differentiate individual bats by their calls, we can enter call counts, information on signal and detection angles, and weather data into generalized random encounter models to estimate bat density. We assessed the utility of generalized random encounter models for estimating Indiana bat (Myotis sodalis) population density with acoustic data collected at 51 total sites in six conservation areas in northeast Missouri, 2019–2021. We tested the effects of year, volancy period, conservation area, and their interactions on estimated density. Volancy period was the best predictor, with average predicted density increasing 60% from pre‐volancy (46 bats/km2) to post‐volancy (74 bats/km2); however, the magnitude of the effect differed by conservation area. We showed that passive acoustic surveys yield informative density estimates that are responsive to temporal changes in bat population size, which suggests this method may be useful for long‐term monitoring. However, we need more information to choose the most appropriate values for the density estimation formula. Future work to refine this approach should include assessments of bat behavior and detection parameters and testing the method's efficacy in areas where population sizes are known.
See the Pet in the Beast : How to Limit Effects of Aliasing

Aliasing is quite powerful, but difficult to control. Often clients need exclusive access to objects for some concerns, and sometimes we see no other way than to ensure this by controlling aliasing. Instead, we propose to restrict what clients can do when accessing objects. To invoke methods in an object clients need tokens issued by this object. Static type checking enforces the tokens to be available and ensures exclusive access for specific concerns without avoiding aliasing. We show by examples how this concept works and discuss several possibilities to improve its flexibility.
Identifying metabolic dysfunction-associated steatotic liver disease in patients with hypertension and pre-hypertension: An interpretable machine learning approach

Objective Metabolic dysfunction-associated steatotic liver disease (MASLD) is one of the most prevalent liver diseases and is associated with pre-hypertension and hypertension. Our research aims to develop interpretable machine learning (ML) models to accurately identify MASLD in hypertensive and pre-hypertensive populations. Methods The dataset for 4722 hypertensive and pre-hypertensive patients is from subjects in the NAGALA study. Six ML models, including the decision tree, K-nearest neighbor, gradient boosting, naive Bayes, support vector machine, and random forest (RF) models, were used in this study. The optimal model was constructed according to the performances of models evaluated by K-fold cross-validation (k = 5), the area under the receiver operating characteristic curve (AUC), average precision (AP), accuracy, sensitivity, specificity, and F1. Shapley additive explanation (SHAP) values were employed for both global and local interpretation of the model results. Results The prevalence of MASLD in hypertensive and pre-hypertensive patients was 44.3% (362 cases) and 28.3% (1107 cases), respectively. The RF model outperformed the other five models with an AUC of 0.889, AP of 0.800, accuracy of 0.819, sensitivity of 0.816, specificity of 0.821, and F1 of 0.729. According to the SHAP analysis, the top five important features were alanine aminotransferase, body mass index, waist circumference, high-density lipoprotein cholesterol, and total cholesterol. Further analysis of the feature selection in the RF model revealed that incorporating all features leads to optimal model performance. Conclusions ML algorithms, especially RF algorithm, improve the accuracy of MASLD identification, and the global and local interpretation of the RF model results enables us to intuitively understand how various features affect the chances of MASLD in patients with hypertension and pre-hypertension.
Geospatial environmental complexity, spatial brain volume, and spatial behavior across the Alzheimer's disease spectrum

Abstract INTRODUCTION Understanding impact of environmental properties on Alzheimer's disease (AD) is paramount. Spatial complexity of one's routinely navigated environment is an important but understudied factor. METHODS A total of 660 older adults from National Alzheimer's Coordinating Center (NACC) dataset were geolocated and environmental complexity index derived from geospatial network landmarks and points‐of‐interest. Latent models tested mediation of spatial navigation‐relevant brain volumes and diagnosis (cognitively‐healthy, mild cognitive impairment [MCI], AD) on effect of environmental complexity on spatial behavior. RESULTS Greater environmental complexity was selectively associated with larger allocentric (but not egocentric) navigation‐related brain volumes, lesser diagnosis of MCI and AD, and better spatial behavioral performance, through indirect hierarchical mediation. DISCUSSION Findings support hypothesis that spatially complex environments positively impact navigation neural circuitry and spatial behavior function. Given the vulnerability of these very circuits to AD pathology, residing in spatially complex environments may be one factor to help stave off the brain atrophy that accompanies spatial navigation deficits across the AD spectrum.
Detecting User Intention at Public Displays from Foot Positions

Imagine an interactive display at a bus station that always provides content users want to see without requiring explicit input. For example, it might provide quick facts to hasty passersby or entertainment to a waiting user. Public display research aims to use contextual awareness to effectively communicate content using public displays, to catch users’ attention, and to motivate more users to interact with the display, all while dealing with the public nature of interaction. Recent research suggests that personal device design paradigms are ineffective for the distracted users of public displays, which would explain the low usage rate of currently deployed systems [1]. Predicting users’ intentions is a powerful tool for satisfying their needs. Body language detection can be used to automatically interpret user behavior and react accordingly; gaze can be used to predict the level of attention, or proxemics can be used to measure user awareness. However, public display users’ complex behaviors, for example inobservance or maintaining social roles, pose new challenges to sensing and interpreting human behavior. For example, distractions while in public can make gaze insufficient to indicate the focus of a user’s attention. Feet have not been highlighted in public display interactions, even though they richly embody expressivity of people’s interests when interacting with other people [2, 3]. Because foot positions are an unconscious expression of body language and thus unaffected by distractions, as a measure of interest they are more robust in a public environment. This work sheds new light on the potential of sensing public display users’ intentions by measuring the positions of their feet. Analyzing the positions of users’ feet aims at making public displays more adaptive and encouraging more users to interact by enhancing the context-awareness of public displays.
Laparoscopic cytoreductive surgery and hyperthermic intraperitoneal chemotherapy for gastric cancer with intraoperative detection of limited peritoneal metastasis: a Phase II study of CLASS-05 trial

Abstract Background Systemic chemotherapy for gastric cancer with peritoneal metastasis has limited clinical benefit; for those with intraoperative detection of occult peritoneal metastasis, cytoreductive surgery followed by hyperthermic intraperitoneal chemotherapy (HIPEC) is an alternative treatment. However, the feasibility and effects of this modality and criteria for selecting suitable groups remain unclear. This study aimed to explore the safety and efficacy of laparoscopic cytoreductive surgery (L-CRS) followed by HIPEC in gastric cancer with limited peritoneal metastasis, and this study also aimed to determine the optimized cut-off of the peritoneal cancer index. Methods Between March 2017 and November 2019, patients diagnosed with gastric cancer peritoneal metastases by using laparoscopy and the Sugarbaker peritoneal cancer index of ≤12 were eligible for inclusion. All patients received L-CRS (including gastrectomy with D2 lymph node dissection) and resection of visible peritoneal metastasis, followed by post-operative HIPEC, and systemic chemotherapy. The primary end points were median progression-free survival and median survival time, and the secondary outcomes were morbidity and mortality within 30 days after surgery. Results Thirty patients were eligible for analysis, of whom 19 (63.3%) were female, and the overall mean age was 53.0 years. The post-operative morbidity was 20% and the severe complication rate was 10%. The median survival time was 27.0 months with a 2-year overall survival rate of 52.3% and median progression-free survival was 14.0 months with a 2-year progression-free survival of 30.4%. Conclusions L-CRS followed by HIPEC can be safely performed for gastric cancer with limited peritoneal metastasis and potential survival benefits.
Platform development for high-throughput optimization of perfusion processes: Part I: Implementation of cell bleeds in microwell plates.

The promise of continuous processing to increase yields and improve product quality of biopharmaceuticals while decreasing the manufacturing footprint is transformative. Developing and optimizing perfusion operations requires screening various parameters, which is expensive and time-consuming when using benchtop bioreactors. Scale-down models (SDMs) are the most feasible option for high-throughput data generation and condition screening. However, new SDMs mimicking perfusion are required, enabling experiments to be run in parallel. In this study, a method using microwell plates (MWP) operating in semi-perfusion mode with an implemented cell bleed step is presented. A CHO cell line was cultivated in a 24-well MWP (Vw = 1.2 mL) and grown at four high cell density (HCD) setpoints. Quasi steady-state condition was obtained by manually performing cell bleeds followed by a total medium exchange after centrifugation. Further, two HCD setpoints were scaled up (VW = 30 mL), comparing a squared six-well deepwell plate (DWP) to shake flasks (SF). This evaluation showed comparable results between systems (DWP vs. SF) and scales (MWP vs. DWP + SF). The results show that the well-plate-based methods are suitable to perform HCD and quasi steady-state cultivations providing a robust solution to industrially relevant challenges such as cell clone and media selection.
Biosynthesis of Cytidine Diphosphate-6-d-Glucitol for the Capsular Polysaccharides of Campylobacter jejuni

Campylobacter jejuni is a Gram-negative pathogenic bacterium commonly found in chickens and is the leading cause of human diarrheal disease worldwide. The various serotypes of C. jejuni produce structurally distinct capsular polysaccharides (CPSs) on the exterior surfaces of the cell wall. The capsular polysaccharide from C. jejuni serotype HS:5 is composed of a repeating sequence of d-glycero-d-manno-heptose and d-glucitol-6-phosphate. We previously defined the pathway for the production of d-glycero-d-manno-heptose in C. jejuni. Here, we elucidate the biosynthetic pathway for the assembly of cytidine diphosphate (CDP)-6-d-glucitol by the combined action of two previously uncharacterized enzymes. The first enzyme catalyzes the formation of CDP-6-d-fructose from cytidine triphosphate (CTP) and d-fructose-6-phosphate. The second enzyme reduces CDP-6-d-fructose with NADPH to generate CDP-6-d-glucitol. Using sequence similarity network (SSN) and genome neighborhood network (GNN) analyses, we predict that these pairs of proteins are responsible for the biosynthesis of CDP-6-d-glucitol and/or CDP-d-mannitol in the lipopolysaccharides (LPSs) and capsular polysaccharides in more than 200 other organisms. In addition, high resolution X-ray structures of the second enzyme are reported, which provide novel insight into the manner in which an open-chain nucleotide-linked sugar is harbored in an active site cleft.
Relationships between physical fitness characteristics, technical skill attributes, and sports injury in female Australian football players

Objectives To explore the relationships between physical fitness and i) technical skills and ii) time-loss from Australian football injury in female players across the talent and participation pathways. Methods This study uses a subset of data from two cross-sectional and one prospective cohort studies. A total of 223 female Australian football players across five competition levels (elite/non-elite senior, high-level junior, and non-elite junior (14–17 years)/(10–13 years)) were included in this study. Comprehensive physical fitness assessments and modified Australian football kicking and handballing tests were conducted in the 2018/19 pre-seasons. During the respective competitive in-season, time-loss injuries were recorded by team personnel. Stepwise multiple linear regressions were performed to determine the relationship between physical fitness and kicking and handballing scores. Cox proportional regressions were conducted to identify physical fitness factors associated with injury. Results Increased running vertical jump height, greater hip abduction strength, and faster timed 6 m hop speed demonstrated a relationship with kicking accuracy when adjusted for years of Australian football playing experience (adjusted R2 = 0.522, p < 0.001). Faster agility time and increased lean mass were associated with better handballing accuracy (adjusted R2 = 0.221, p < 0.001). Multivariate Cox regression revealed an increased risk for sustaining a time-loss injury in less agile players (adjusted HR 2.41, 95% CI 1.23, 4.73, p = 0.010). However, this relationship no longer remained when adjusted for age and years of Australian football experience (adjusted HR 1.68, 95% CI 0.81, 3.50, p = 0.166). Conclusions Physical fitness may be a significant factor contributing to development of Australian football technical skills in female players. However, its role is unclear in protecting against injury risk in this athlete population. Further research is needed to explore the multifactorial and complex phenomenon of talent development and injury risk reduction in female Australian football players.
TRIM27 elicits protective immunity against tuberculosis by activating TFEB-mediated autophagy flux.

Infectious diseases, such as Mycobacterium tuberculosis (Mtb)-caused tuberculosis (TB), remain a global threat exacerbated by increasing drug resistance. Host-directed therapy (HDT) is a promising strategy for infection treatment through targeting host immunity. However, the limited understanding of the function and regulatory mechanism of host factors involved in immune defense against infections has impeded HDT development. Here, we identify the ubiquitin ligase (E3) TRIM27 (tripartite motif-containing 27) as a host protective factor against Mtb by enhancing host macroautophagy/autophagy flux in an E3 ligase activity-independent manner. Mechanistically, upon Mtb infection, nuclear-localized TRIM27 increases and functions as a transcription activator of TFEB (transcription factor EB). Specifically, TRIM27 binds to the TFEB promoter and the TFEB transcription factor CREB1 (cAMP responsive element binding protein 1), thus enhancing CREB1-TFEB promoter binding affinity and promoting CREB1 transcription activity toward TFEB, eventually inducing autophagy-related gene expression as well as autophagy flux activation to clear the pathogen. Furthermore, TFEB activator 1 can rescue TRIM27 deficiency-caused decreased autophagy-related gene transcription and attenuated autophagy flux, and accordingly suppressed the intracellular survival of Mtb in cell and mouse models. Taken together, our data reveal that TRIM27 is a host defense factor against Mtb, and the TRIM27-CREB1-TFEB axis is a potential HDT-based TB target that can enhance host autophagy flux.
The Information Systems Design Science Research Body Of Knowledge - A Citation Analysis In Recent Top-Journal Publications

Information systems (IS) design science research (ISDSR) has received particular attention after the seminal MISQ article by Hevner et al. (2004). Since then, several articles on ISDSR have appeared in top IS journals. This paper examines the development of a common body of knowledge in ISDSR. To this end, a citation analysis in recently published ISDSR articles in top IS journals is conducted. According to Kuhn (1970), a common body of knowledge is important for each emerging scientific discipline. For the purposes of delineating ISDSR from other types of IS research, the author develops three criteria. Based upon these criteria, he identifies 50 ISDSR articles published in the journals of the basket of the six from 2007-2010. An analysis of the citations of these articles reveals that Hevner et al. (2004) is cited as a source in 70% of all articles. Moreover, six further specific publications are cited by more than 10% of all articles. The author argues that ISDSR is developing a common body of knowledge and does have a cumulative tradition.
Converting a trained neural network to a decision tree dectext - decision tree extractor

Neural Networks are very successful in acquiring hidden knowledge in datasets. Their most important weakness is that the knowledge they acquired is represented in a form not very understandable to humans. In real life applications understandability of the knowledge acquired by a machine learning system is important. In this thesis, we address the understandability problem of neural networks by converting the knowledge acquired by the neural network into a more understandable form. We do this by extracting a classical decision tree from neural network. We tested the new method on neural networks trained by using real life datasets. We showed that the decision trees extracted have high fidelity to the neural networks they are extracted from. Fidelity measures the closeness of decision tree's outputs to neural network's outputs. We introduced four new splitting techniques to make DecText increase the fidelity of the extracted trees. We showed that the new methods are effective in extracting high fidelity trees. We also introduced a new discretization technique to make DecText be able to handle continuous features. New discretization technique can be used in other induction algorithms if a neural network is trained by using the dataset. We also introduced a new technique to prune the trees extracted. New pruning technique tries to find the simplest tree with the highest fidelity. Another contribution of this thesis is a new feature subset selection technique. Real world classification applications usually have many features. This increases the complexity of the classification task. Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge. We tested the new technique on real world and artificial datasets and compared its results with existing methods. We showed that the new method chooses good subsets by searching fewer states than the existing methods. In the new method, we first sort the features according to their relevance and test the subsets formed by the most relevant features to find a starting subset for searching the subset space. We show that this technique speeds up the search considerably for most of the problem domains.
A Study of In-Situ Visualization for Petascale Combustion Simulations

As scientific supercomputing moves toward petascale and exascale, in-situ visualization clearly becomes the most costeffective way to enable scientists see the full extent of the data generated by their simulations. This is particularly crucial for capturing and understanding highly intermittent transient phenomena, e.g. ignition/extinction events in turbulent combustion. However, integrating the visualization process into the simulation pipeline poses several challenges which have not been fully addressed. In this paper, we present a complete case study of in-situ visualization for a petascale combustion simulation. We describe our design decisions and optimization strategies on domain decomposition, rendering, and image compositing. We present an indepth evaluation of our implementation on the Cray XT5 at the NCCS/ORNL, and discuss the lessons learned through this study. We demonstrate that in-situ visualization is a feasible solution and our work provides a valuable guideline to those who will pursue a similar solution.
Multi-touch surface based on RGBD camera

The popularity of interactive surfaces is increasing due to their user-friendliness and the lack of need to use special controllers. With appropriate technologies we can add multi-touch functionality to almost any surface. Today numerous museums use interactive surfaces to display the information on user-interesting ways. Number of schools and companies using interactive tables is increasing, due to the positive effects the usage has on the enhancement of the interest of participants. Interactive tables, walls and floors are usually useful for advertising purposes as well as they always attract crowds. In addition to more established two-dimensional interactive surfaces, three-dimensional are slowly emerging as well. With the release of low-cost RGBD cameras they are becoming increasingly available to general public. They allow the users to interact with the system not only by touching the surface but also by adjusting the distance of the fingers to the surface. This diploma thesis describes the development of a system for 3D human-computer interaction. The system is capable of detecting and tracking user's fingertips and thus creating user-friendly and affordable multi-touch surface. Three-dimensional information of the observed scene is captured by RBGD camera Kinect. Information about the detected fingertips is transmitted to the target application over TUIO protocol. System is evaluated based on the accuracy of detections, response time, and user experience. During the assessment it proved to be very accurate and responsive enough to be used in everyday applications.
Experiments on Morphological Reinflection: CoNLL-2017 Shared Task

We present two systems for the task of morphological inflection, i.e., finding a target morphological form, given a lemma and a set of target tags. Both are trained on datasets of three sizes: low, medium and high. The first uses a simple Long Short-Term Memory (LSTM) for lowsized dataset, while it uses an LSTMbased encoder-decoder based model for the medium and high sized datasets. The second uses a simple Gated Recurrent Unit (GRU) for low-sized data, while it uses a combination of simple LSTMs, simple GRUs, stacked GRUs and encoderdecoder models, depending on the language, for medium-sized data. Though the systems are not very complex, they give accuracies above baseline accuracies on high-sized datasets, around baseline accuracies for medium-sized datasets but mostly accuracies lower than baseline for low-sized datasets.