Miracle-dz/UniFAR-FTUs huggingface dataset:
Population Dynamics of a Declining White-Tailed Deer Population in the Southern Appalachian Region of the United States

Simple Summary We examined a white-tailed deer (Odocoileus virginianus) population with evidence of decline in the Southern Appalachian region of the United States. In the Chattahoochee National Forest (CNF) in northern Georgia, deer harvest numbers and hunter success rates have drastically reduced over the last few decades, suggesting a decline in deer density. Low fawn survival (16%) was also recently recorded, prompting further concern regarding deer population sustainability. In the CNF, we radio-collared 14 yearling and 45 adult female white-tailed deer along with 71 fawns during 2018–2020 to estimate the annual survival and fecundity of each age class. We modeled future population growth (λ) over 10 years to evaluate the current rate of decline and various other management scenarios. Projections indicated that the white-tailed deer population will decline by an average of 4.0% annually under current conditions, and only scenarios that incorporated both antlerless harvest restrictions and improved fawn survival will lead to positive population growth. Thus, these approaches should be emphasized in future management plans within the Southern Appalachian region to facilitate recovery. This methodology can be applied by other wildlife managers with knowledge of site-specific vital rates to inform potential management strategies. Abstract Although generally abundant, white-tailed deer (Odocoileus virginianus) populations in the southeastern United States have recently experienced several localized declines attributed to reduced fawn recruitment following the establishment of coyotes (Canis latrans). The Southern Appalachians is a mountainous region suggested to be experiencing white-tailed deer declines, as harvest numbers and hunter success rates have substantially decreased in northern Georgia since 1979. Low fawn survival (16%) was also recently documented in the Chattahoochee National Forest (CNF) in northern Georgia, necessitating further examination. We radio-collared 14 yearling and 45 adult female white-tailed deer along with 71 fawns during 2018–2020 in the CNF to estimate field-based vital rates (i.e., survival and fecundity) and parameterize stage-structured population models. We projected population growth rates (λ) over 10 years to evaluate the current rate of decline and various other management scenarios. Our results indicated that the observed population would decline by an average of 4.0% annually (λ = 0.960) under current conditions. Only scenarios including antlerless harvest restrictions in addition to improved fawn survival resulted in positive growth (λ = 1.019, 1.085), suggesting these measures are likely necessary for population recovery in the region. This approach can be applied by wildlife managers to inform site-specific management strategies.
ECG-Based Multiclass Arrhythmia Classification Using Beat-Level Fusion Network

Cardiovascular disease (CVD) is one of the most severe diseases threatening human life. Electrocardiogram (ECG) is an effective way to detect CVD. In recent years, many methods have been proposed to detect arrhythmia using 12-lead ECG. In particular, deep learning methods have been proven to be effective and have been widely used. The attention mechanism has attracted extensive attention in many fields in a series of deep learning methods. Off-the-shelf solutions based on deep learning and attention mechanism for ECG classification mostly give weights to time points. None of the existing methods were considered using the attention mechanism dealing with ECG signals at the level of heartbeats. In this paper, we propose a beat-level fusion net (BLF-Net) for multiclass arrhythmia classification by assigning weights at the heartbeat level, according to the contribution of the heartbeat to diagnostic results. This algorithm consists of three steps: (1) segmenting the long ECG signal into short beats; (2) using a neural network to extract features from heartbeats; and (3) assigning weights to features extracted from heartbeats using an attention mechanism. We test our algorithm on the PTB-XL database and have superiority over state-of-the-art performance on six classification tasks. Besides, the principle of this architecture is clarified by visualizing the weight of the attention mechanism. The proposed BLF-Net is shown to be useful and automatically provides an effective network structure for arrhythmia classification, which is capable of aiding cardiologists in arrhythmia diagnosis.
Determining magnitude from Lg

In this paper, an empirical method is developed for determining magnitude of seismic events from the amplitude of Lg phases recorded at local to regional distances from the events. The data used were obtained from Long Range Seismic Measurements (LRSM) reports for 78 seismic events associated with explosions conducted the Atomic Energy Commission. Preliminary investigation showed that magnitudes (M) calculated from Lg recordings had less scatter than body-wave magnitudes (m) reported for the same events and stations. A sixth-degree polynomial was found to give the best fit to log amplitude/period (A/T) values plotted as a function of epicentral distance. Amplitude-distance correction factors (Q) are presented in a chart. Magnitudes from Lg were compared to the average unified magnitudes from LRSM shot reports. For 60 of 68 events, the standard deviation for M was less than that for m. The M values for all of the events were normalized to magnitude 5.0, and average residuals were found for 69 reporting stations. A plot of the residuals on a map indicates that they represent station corrections rather than azimuthal variations. Apparent station corrections (s) are presented for 23 LRSM stations.
Association of retinal vessel pathology and brain atrophy in relapsing-remitting multiple sclerosis

Background Optical coherence tomography angiography (OCTA) allows non-invasive assessment of retinal vessel structures. Thinning and loss of retinal vessels is evident in eyes of patients with multiple sclerosis (MS) and might be associated with a proinflammatory disease phenotype and worse prognosis. We investigated whether changes of the retinal vasculature are linked to brain atrophy and disability in MS. Material and methods This study includes one longitudinal observational cohort (n=79) of patients with relapsing-remitting MS. Patients underwent annual assessment of the expanded disability status scale (EDSS), timed 25-foot walk, symbol digit modalities test (SDMT), retinal optical coherence tomography (OCT), OCTA, and brain MRI during a follow-up duration of at least 20 months. We investigated intra-individual associations between changes in the retinal architecture, vasculature, brain atrophy and disability. Eyes with a history of optic neuritis (ON) were excluded. Results We included 79 patients with a median disease duration of 12 (interquartile range 2 - 49) months and a median EDSS of 1.0 (0 - 2.0). Longitudinal retinal axonal and ganglion cell loss were linked to grey matter atrophy, cortical atrophy, and volume loss of the putamen. We observed an association between vessel loss of the superficial vascular complex (SVC) and both grey and white matter atrophy. Both observations were independent of retinal ganglion cell loss. Moreover, patients with worsening of the EDSS and SDMT revealed a pronounced longitudinal rarefication of the SVC and the deep vascular complex. Discussion ON-independent narrowing of the retinal vasculature might be linked to brain atrophy and disability in MS. Our findings suggest that retinal OCTA might be a new tool for monitoring neurodegeneration during MS.
HIV-1 infection of genetically engineered iPSC-derived central nervous system-engrafted microglia in a humanized mouse model

ABSTRACT The central nervous system (CNS) is a major human immunodeficiency virus type-1 (HIV-1) reservoir. Microglia are the primary target cell of HIV-1 infection in the CNS. Current models have not allowed the precise molecular pathways of acute and chronic CNS microglial infection to be tested with in vivo genetic methods. Here, we describe a novel-humanized mouse model utilizing human induced pluripotent stem cell (iPSC)-derived microglia to xenograft into murine hosts. These mice are additionally engrafted with human peripheral blood mononuclear cells that serve as a medium to establish a peripheral infection that then spreads to the CNS microglia xenograft, modeling a trans-blood-brain barrier route of acute CNS HIV-1 infection with human target cells. The approach is compatible with iPSC genetic engineering, including inserting targeted transgenic reporter cassettes to track the xenografted human cells, enabling the testing of novel treatment and viral tracking strategies in a comparatively simple and cost-effective in vivo model for neuroHIV. IMPORTANCE Our mouse model is a powerful tool for investigating the genetic mechanisms governing central nervous system (CNS) human immunodeficiency virus type-1 (HIV-1) infection and latency in the CNS at a single-cell level. A major advantage of our model is that it uses induced pluripotent stem cell-derived microglia, which enables human genetics, including gene function and therapeutic gene manipulation, to be explored in vivo, which is more challenging to study with current hematopoietic stem cell-based models for neuroHIV. Our transgenic tracing of xenografted human cells will provide a quantitative medium to develop new molecular and epigenetic strategies for reducing the HIV-1 latent reservoir and to test the impact of therapeutic inflammation-targeting drug interventions on CNS HIV-1 latency. Our mouse model is a powerful tool for investigating the genetic mechanisms governing central nervous system (CNS) human immunodeficiency virus type-1 (HIV-1) infection and latency in the CNS at a single-cell level. A major advantage of our model is that it uses induced pluripotent stem cell-derived microglia, which enables human genetics, including gene function and therapeutic gene manipulation, to be explored in vivo, which is more challenging to study with current hematopoietic stem cell-based models for neuroHIV. Our transgenic tracing of xenografted human cells will provide a quantitative medium to develop new molecular and epigenetic strategies for reducing the HIV-1 latent reservoir and to test the impact of therapeutic inflammation-targeting drug interventions on CNS HIV-1 latency.
Robust Logistic Regression for Graduate's Employability from Public Universities in Malaysia

The graduate’s employability is studied to investigate its factors involving graduates from public universities in Malaysia for the year 2016. This topic has become a concerning topic in Malaysia as the number of graduates getting hired is lower than expected and does not commensurate with the total number of graduates produced every year. The investigation on graduate’s employability based on their profiles is done by utilizing a robust method due to the existence of outliers in the data set. The analysis shows that age, CGPA, discipline, educational stage, English grade, entry qualification, gender, state, sponsor and university types are factors that affect Malaysian public university graduates. Overall, robust logistic model fits the data with correctly classified 69.65% and produces area under the Receiver Operating Characteristics (ROC) curve with 0.6961 in separating the groups of employed and unemployed graduates.
Deep Dynamic Adaptation Network Based on Joint Correlation Alignment for Cross-Scene Hyperspectral Image Classification

Deep learning methods face significant challenges in practical cross-scene classification tasks of hyperspectral images (HSIs), primarily due to the difficulty of acquiring labels and the issue of inconsistent distribution caused by spectral drift. To tackle the above issues, we propose a deep dynamic adaptation network based on joint correlation alignment (DDAN-JCA) for cross-scene HSI classification. First, the dual-channel residual network (DCRN) and the attention mechanism module (AMM) are employed to extract spatial–spectral joint features from both the source domain (SD) and target domain (TD). Then, the method of correlation alignment (CORAL) is employed to minimize the marginal distribution discrepancy between two domains and further reduce the conditional distribution discrepancy of each class. Finally, a dynamic distribution adaptation strategy is used to dynamically adjust the importance of marginal distribution and conditional distribution by using a balance factor. DDAN-JCA can achieve unsupervised classification without using target labels. The performance of DDAN-JCA has been validated using three hyperspectral datasets, and the experimental results demonstrate that DDAN-JCA significantly enhances classification accuracy and exhibits greater robustness compared with state-of-the-art methods.
Agricultural innovation for climate change mitigation and adaptation: A comparison of New Zealand and California farmers and policies

2009Relevant Consulting Experience: Transformative Climate Adaptation for Crop Systems, World Resources Institute & TetraTech, (2018) Climate Change and Food Systems Impacts and Opportunities, Meridian Institute & Global Alliance for the Future of Food, (2017-2018) Nitrogen Management Carbon Offset Protocol Expansion, Environmental Defense Fund, San Francisco, CA (2016-2018) Climate Change Mitigation Through Livestock, Food Waste and Dietary Changes, Natural Resources Defense Council, San Francisco, CA (2014-2015) Nitrogen Management Project Offset Protocol, Climate Action Reserve, Los Angeles, CA (2013-2014) Environment and Sustainability Consultant, Millstone Advisors, New York, NY (2012-2013) Grasslands Carbon Offset Protocol, Climate Action Reserve, Los Angeles, CA (2012) Environmental Consultant-Manure Management, Organic Valley Dairy Cooperative, La Farge, WI (2011) Food and Environment Consultant, Clif Bar Company, Berkeley, CA (2011) Social Network Analyst, Agency for Educational Development/US Agency for International Development, Washington DC, (2010) Agriculture and Science Consultant, National Cooperative Grocers Association, Iowa City, IA (20092011)
Manual Lymph Drainage for Post-COVID-19 Related Cough, Breathlessness, and Fatigue; Two Case Reports

Background: Persistent symptoms after SARS CoV-2 infection such as fatigue, shortness of breath, and cognitive dysfunction that cannot be explained by an alternative diagnosis have been termed long COVID and present a significant emerging public health problem. Current approaches include rehabilitation and symptom management involving multiple health disciplines and as yet there are no pharmaceutical approaches other than routine symptom management. Manual lymph drainage (MLD) has been used to support recovery during pulmonary rehabilitation and reduce chronic inflammation including symptoms associated with long COVID. Case description and outcomes: Two adult females who had reported long-COVID symptoms more than 10 weeks after the resolution of the acute infection were treated with MLD by Remedial Therapists trained in the Dr Vodder method of MLD. Respiratory function (Peak Flow Meter) and blood oxygen levels (Oximeter) were recorded before and after a one-minute sit-to-stand test prior to the treatment. The Dyspnea-12 Questionnaire, the Revised Piper Fatigue Scale, and Likert scales were used to collect client-reported outcomes. Six 45-min treatments were applied weekly, with a follow-up review and treatment at three months. In both cases, all outcomes improved after the third treatment with further improvement noted at three months. Conclusions: MLD may offer a non-invasive, non-pharmaceutical approach to the resolution of long-COVID symptoms such as cough, breathlessness, and fatigue.
Structural behavior of UHPC epoxy composite plate subjected to velocity impact loading

This article aims to investigate the structural behavior of Ultra High Performance Epoxy Composite Plate subjected to velocity impact loading. Two types of material were utilized for the composite plate: epoxy, and UHPC. UHPC plate plays as a front layer to strengthen the energy absorption and protect the epoxy layer. The material model used to simulate the epoxy plate was verified by experimental results to prove its accuracy and efficiency. Impact loading was implemented in the Abaqus model by an impactor nose which has 25mm diameter. The projectile was assumed at the velocity of 120 m/s. The damage propagated due to the increment of the impact energy was estimated by the Johnson-Holmsquist concrete (JHC) material model in Abaqus. Maximum deflection, the kinetic energy versus internal energy were also investigated in the current article. Based on the obtained result, the Epoxy material combined with UHPC material was proved to be a potential application in impact resistance design.
A Thematic Review on Predicting Student Performance Using Data Mining

Student performance, a key criterion for academic placement and success, can be effectively predicted and potentially improved using advanced data mining techniques. However, more studies delve into patterns and methods for forecasting student performance in this context. This study presents a comprehensive thematic review of the literature published between 2016 and 2022 to highlight prevailing trends and underline the significant gaps in this rapidly growing research domain. The review emphasizes that the highest number of related publications were seen in 2021, with a clear focus on higher educational institutions. Two major factors were recurrent in these studies: academic records and demographic information of the students, widely recognized as significant determinants of academic success. This observation reaffirms previous research asserting the substantial role of academic records in predicting student performance. Our analysis expands the scope of prior literature reviews by incorporating studies from Mendeley and Science Direct databases. Interestingly, we discovered a glaring research gap in predicting performance in Malaysian secondary schools using locally relevant datasets. Therefore, further exploration in this niche could benefit this geographical context. Furthermore, our review identified the need to investigate other variables that may impact student performance. There is vast potential for unearthing novel insights that could substantially enhance our understanding and predictive accuracy. Consequently, this study emphasizes the importance of continuing research in this field, integrating various datasets, geographical contexts, and potential predictors to better anticipate and address student performance across various educational scenarios.
Implementing deep learning-based named entity recognition for obtaining narcotics abuse data in Indonesia

The availability of drug abuse data from the official website of the National Narcotics Board of Indonesia is not up-to-date. Besides, the drug reports from Indonesian National Narcotics Board are only published once a year. This study aims to utilize online news sites as a data source for collecting information about drug abuse in Indonesia. In addition, this study also builds a named entity recognition (NER) model to extract information from news texts. The primary NER model in this study uses the convolutional neural network-long short-term memory (CNNs-LSTM) architecture because it can produce a good performance and only requires a relatively short computation time. Meanwhile, the baseline NER model uses the bidirectional long short-term memory-conditional random field (Bi-LSTMs-CRF) architecture because it is easy to implement using the Flair framework. The primary model that has been built results in a performance (F1 score) of 82.54%. Meanwhile, the baseline model only results in a performance (F1 score) of 69.67%. Then, the raw data extracted by NER is processed to produce the number of drug suspects in Indonesia from 2018-2020. However, the data that has been produced is not as complete as similar data sourced from Indonesian National Narcotics Board publications.
Field Efficacy Testing of Sex Pheromone Trapping with Alternative Trap Desing-Lure Options for Brinjal Shoot and Fruit Borer in Srilanka

Collaborative field studies were carried out to evaluate the effectiveness of improved pheromone trapping system against brinjal shoot and fruit borer Leucinodes orbonalis Guenee in Sri Lanka. First field study comparing five alternative trap designs in two locations for eight weeks confirmed that moth catch was more in funnel trap with two holders (3.08), with the next being funnel trap with three holders (2.09). The catches in delta trap were intermediate (1.81), while other two funnel traps caught 1.05 to 1.84 moths. The differential efficacy of moth catches may be attributed to trap colour, shape, size and other features enhancing arrivals and/or minimising moth escape. The second field study was on pheromone loadings in lures which showed non -significant differences among 3, 5, 7 and 10 mg loadings. These studies confirmed the superiority of promising trap designs for trapping of the brinjal shoot and fruit borer moth in Sri Lanka. Scope for further research to identify more efficient trap designs and optimum pheromone loadings is indicated.
Sorption studies of Tetraoxophosphate (v) ions on some agricultural soils in Akwa Ibom State, eastern Niger Delta, Nigeria

The phosphate sorption capacity regulates principally the phosphorus concentration and available phosphorus in any soil solution. This work aims to study the phosphate sorption characteristics of five agricultural soils in Akwa Ibom State, Nigeria. The phosphate adsorption isotherm was obtained by shaking each soil sample with 20, 40, 60, 80, 100 and 120 mgL–1 for 24 h with 20 mL of KH2PO4 in 0.01 M CaCl2 solution. The Langmuir, Freundlich, Van Huay and Temkin isotherms were applied to study the PO43-sorption characteristics of the soil samples. The Freundlich model provided the best choice for phosphate sorption with mean R2 value of 0.9872. Results obtained for the phosphate sorbed (µg g–1soil) with 120 mgL-1 solution, heterogeneity factor of adsorption sites (nF), and the Freundlich constant (KF) (Lg–1) were 101(84.3), 0.515 and 7.355 for Onna soil; 62.8(52.3), 0.641 and 2.464 for Ikot-Amama 2; 60.6(50.5), 0.708 and 3.985 for Ogu-Itumbonuso soil; 59.3(49.5), 0.652 and 2.505 for Ikot-Amama 1 soils; 58.0(48.3), 0.704 and 3.682 for Ikot-Nkon soils, respectively. The equilibrium concentration for phosphates (EC-P) were used as desorption index to determine whether the soils could serve as potential sources for their bioavailability to plants and results ranged from 5.0 to 9.90 mgL–1, indicating high vulnerability to phosphate loss. The relationship between phosphate sorption parameters and soil physicochemical properties showed that the Freundlich adsorption intensity factor, nF, strongly correlated negatively with organic carbon and organic matter (r = -0.953 and -0.947; P<0.01), Fe and Mn (r = -0.911 and -0.812, P<0.05). The EC-P correlated strongly with soil pH(H2O) (r = 0.959, P<0.01), clay (r = 0.913, P<0.05), cation exchange capacity (CEC) (r = -0.824, P<0.05), Mn (r = -0.792, P<0.05) and Fe (r = -0.826, P<0.05). The study illustrated that soil management practices which involves the using organic fertilizers as the basis for PO43- sorption in relation to soil properties can be a useful tool for sustainable crop production and environmental soil-water contamination.