Miracle-dz/UniFAR-FTUs huggingface dataset:
Malaria Risk Stratification and Modeling the Effect of Rainfall on Malaria Incidence in Eritrea

Background Malaria risk stratification is essential to differentiate areas with distinct malaria intensity and seasonality patterns. The development of a simple prediction model to forecast malaria incidence by rainfall offers an opportunity for early detection of malaria epidemics. Objectives To construct a national malaria stratification map, develop prediction models and forecast monthly malaria incidences based on rainfall data. Methods Using monthly malaria incidence data from 2012 to 2016, the district level malaria stratification was constructed by nonhierarchical clustering. Cluster validity was examined by the maximum absolute coordinate change and analysis of variance (ANOVA) with a conservative post hoc test (Bonferroni) as the multiple comparison test. Autocorrelation and cross-correlation analyses were performed to detect the autocorrelation of malaria incidence and the lagged effect of rainfall on malaria incidence. The effect of rainfall on malaria incidence was assessed using seasonal autoregressive integrated moving average (SARIMA) models. Ljung–Box statistics for model diagnosis and stationary R-squared and Normalized Bayesian Information Criteria for model fit were used. Model validity was assessed by analyzing the observed and predicted incidences using the spearman correlation coefficient and paired samples t-test. Results A four cluster map (high risk, moderate risk, low risk, and very low risk) was the most valid stratification system for the reported malaria incidence in Eritrea. Monthly incidences were influenced by incidence rates in the previous months. Monthly incidence of malaria in the constructed clusters was associated with 1, 2, 3, and 4 lagged months of rainfall. The constructed models had acceptable accuracy as 73.1%, 46.3%, 53.4%, and 50.7% of the variance in malaria transmission were explained by rainfall in the high-risk, moderate-risk, low-risk, and very low-risk clusters, respectively. Conclusion Change in rainfall patterns affect malaria incidence in Eritrea. Using routine malaria case reports and rainfall data, malaria incidences can be forecasted with acceptable accuracy. Further research should consider a village or health facility level modeling of malaria incidence by including other climatic factors like temperature and relative humidity.
Destabilisation of Creeping Permafrost: The Plator Rock Glacier Case Study (Central Italian Alps)

The Plator rock glacier is the first such landform identified in the Italian Alps that shows destabilised behaviour. Analysis of six sets of sequential orthophotographs from 1981 to 2012 reveals an exceptional advance of the rock glacier front (92.1 m) and a horizontal velocity up to 4 m a‐1 in different zones. The spatial variability of kinematics was evaluated by tracking sets of ‘tracer’ boulders on the rock glacier through time. Its velocity has progressively increased from the rooting zone to the tongue, with complex trends associated with distinct morphological features. Destabilisation likely occurred between 1954 and 1981, probably due to the relatively low elevation of the tongue, which resulted in warm permafrost conditions. Field observations reveal the presence of a large rock fall deposit, which occurred before 1981, and suggest that the debris overload could have triggered destabilisation. Since June 2015, an intensive monitoring programme has been implemented on the rock glacier, as the tongue is expected to travel over a steeper slope segment within the next 3 to 5 years, which could evolve in a catastrophic movement. Copyright © 2016 John Wiley & Sons, Ltd.
Consecutive digital infrared thermography in patients withtemporomandibular joint arthralgia

The objective of this study was to investigate the changes of skin temperatures over the temporomandibular joint (TMJ) and clinical findings consecutively during conservative treatments in unilateral TMJ arthralgia patients. The study enrolled 31 patients with unilateral TMJ arthralgia and 26 healthy control subjects based on the Research Diagnostic Criteria for Temporomandibuar Disorders. The measurements of skin temperatures over the TMJ and clinical examinations were performed in all subjects at baseline, 2 and 4 weeks later, and patients were given conservative treatments. Thermographic examinations were performed before and after chewing activity for 5 minutes in each session. The data were analyzed by repeated-measures ANOVA. In affected TMJ, the change in temperature between before and after chewing activity was significantly high in TMJ arthralgia group and decreased after 2 and 4 weeks. Thermal asymmetry measured before chewing activity showed insignificant difference between groups. However, those measured after chewing activity were significantly high in TMJ arthralgia group and reduced to the similar level of control group after 2 and 4 weeks. After chewing activity, both skin temperature over painful TMJ and the extent of thermal asymmetry in TMJ arthralgia patients were higher than those of control group. These thermal indices of TMJ arthralgia patients were reduced in accordance with clinical improvements by conservative treatments. Consecutive thermography accompanying chewing activity which may increase TMJ pain temporarily can be a useful adjunctive technique to diagnose TMJ arthralgia and evaluate its clinical progression including level of pain.
The relationship between mitochondrial respiration, resting metabolic rate and blood cell count in great tits

ABSTRACT Although mitochondrial respiration is believed to explain a substantial part of the variation in resting metabolic rate (RMR), few studies have empirically studied the relationship between organismal and cellular metabolism. We therefore investigated the relationship between RMR and mitochondrial respiration of permeabilized blood cells in wild great tits (Parus major L.). We also studied the correlation between mitochondrial respiration traits and blood cell count, as normalizing mitochondrial respiration by the cell count is a method commonly used to study blood metabolism. In contrast to previous studies, our results show that there was no relationship between RMR and mitochondrial respiration in intact blood cells (i.e. with the ROUTINE respiration). However, when cells were permeabilized and interrelation re-assessed under saturating substrate availability, we found that RMR was positively related to phosphorylating respiration rates through complexes I and II (i.e. OXPHOS respiration) and to the mitochondrial efficiency to produce energy (i.e. net phosphorylation efficiency), though variation explained by the models was low (i.e. linear model: R2=0.14 to 0.21). However, unlike studies in mammals, LEAK respiration without [i.e. L(n)] and with [i.e. L(Omy)] adenylates was not significantly related to RMR. These results suggest that phosphorylating respiration in blood cells can potentially be used to predict RMR in wild birds, but that this relationship may have to be addressed in standardized conditions (permeabilized cells) and that the prediction risks being imprecise. We also showed that, in our conditions, there was no relationship between any mitochondrial respiration trait and blood cell count. Hence, we caution against normalising respiration rates using this parameter as is sometimes done. Future work should address the functional explanations for the observed relationships, and determine why these appear labile across space, time, taxon, and physiological state.
Daily Functional Characteristics and Health Among Older Adults During COVID-19: A Structural Equation Modeling Approach

Within a few years, 1 in 6 people will be aged 60 years or older. Extreme situations, such as the COVID-19 crisis, constitute a challenge to older adults. However, the literature on the daily functional characteristics of older adults in the past and during the COVID-19 crisis and their relationships to their physical and mental health is scarce. This study aimed to examine the past and present daily functional factors associated with physical and mental health in older adults. Using an online platform, 204 Israelis aged 60 years and older reported their physical health symptoms and anxiety levels. They completed questionnaires about past (negative life events and childhood daily functional self-actualization) and present (adulthood daily functional self-actualization, functional cognition and sleep quality) factors. Structural equation modeling revealed correlations between functional cognition and childhood daily functional self-actualization (β = −.18) and anxiety (β = .15); adulthood daily functional self-actualization and past negative life events (β = −.18), anxiety (β = −.50), and physical symptoms (β = −16); and sleep quality and past childhood daily functional self-actualization (β = −.19), negative life events (β = .22), anxiety (β = .18), and physical symptoms (β = .40). These findings shed light on potential functional factors for older adults’ health, indicating that these functional factors play a vital role in reducing health problems in later life.
The role of the Indian Ocean sector for prediction of the coupled Indo‐Pacific system: Impact of atmospheric coupling

Indian Ocean (IO) dynamics impact ENSO predictability by influencing wind and precipitation anomalies in the Pacific. To test if the upstream influence of the IO improves ENSO validation statistics, a combination of forced ocean, atmosphere, and coupled models are utilized. In one experiment, the full tropical Indo-Pacific region atmosphere is forced by observed interannual SST anomalies. In the other, the IO is forced by climatological SST. Differences between these two forced atmospheric model experiments spotlight a much richer wind response pattern in the Pacific than previous studies that used idealized forcing and simple linear atmospheric models. Weak westerlies are found near the equator similar to earlier literature. However, at initialization strong easterlies between 30°S to 10°S and 0°N to 25°N and equatorial convergence of the meridional winds across the entire Pacific are unique findings from this paper. The large-scale equatorial divergence west of the dateline and northeasterly-to-northwesterly cross-equatorial flow converging on the equator east of the dateline in the Pacific are generated from interannual IO SST coupling. In addition, off-equatorial downwelling curl impacts large-scale oceanic waves (i.e. Rossby waves reflect as western boundary Kelvin waves). After 3 months, these downwelling equatorial Kelvin waves propagate across the Pacific and strengthen the NINO3 SST. Eventually Bjerknes feedbacks take hold in the eastern Pacific which allows this warm anomaly to grow. Coupled forecasts for NINO3 SST anomalies for 1993-2014 demonstrate that including interannual IO forcing significantly improves predictions for 3-9 month lead times. This article is protected by copyright. All rights reserved.
Application and Verification of Techniques for Visually Assessing Pasture Conditions in Mountainous Terrain: A Test of Three Field Assessment Methods in the Kyrgyz Republic

The objective of this study was to determine a reliable and effective way to visually assess pastures under the site conditions that prevail in the Kyrgyz Republic, in particular the mountainous terrain. Such a method should make a visual evaluation of pasture conditions in the field possible and help build awareness among land users of soil and pasture degradation and the need for sustainable use of pasture areas. To this end, the Visual Soil Assessment (VSA) method was applied and verified in a research area in the Naryn district in the Kyrgyz Republic. This process included the application of 2 additional visual site assessment methods—the Muencheberg Soil Quality Rating and the method described in the Monitoring Manual for Summer Pastures in the Greater Caucasus in Azerbaijan by Etzold and Neudert in 2010, referred to subsequently as the MMSP method. A comparison of the VSA results to standard field measurements and laboratory-based analysis was also performed. The VSA method was found to be only moderately applicable under the site conditions, with low correlation between the indicator ratings and the results of the standard measurements. The MMSP method showed substantially better applicability. The results of the study suggest that further research is needed to refine the MMSP method to develop an effective visual assessment method that can be used to support sustainable use and protection of pasture areas in mountain regions.
Language model enables end-to-end accurate detection of cancer from cell-free DNA

Abstract We present a language model Affordable Cancer Interception and Diagnostics (ACID) that can achieve high classification performance in the diagnosis of cancer exclusively from using raw cfDNA sequencing reads. We formulate ACID as an autoregressive language model. ACID is pretrained with language sentences that are obtained from concatenation of raw sequencing reads and diagnostic labels. We benchmark ACID against three methods. On testing set subjected to whole-genome sequencing, ACID significantly outperforms the best benchmarked method in diagnosis of cancer [Area Under the Receiver Operating Curve (AUROC), 0.924 versus 0.853; P < 0.001] and detection of hepatocellular carcinoma (AUROC, 0.981 versus 0.917; P < 0.001). ACID can achieve high accuracy with just 10 000 reads per sample. Meanwhile, ACID achieves the best performance on testing sets that were subjected to bisulfite sequencing compared with benchmarked methods. In summary, we present an affordable, simple yet efficient end-to-end paradigm for cancer detection using raw cfDNA sequencing reads.
Chemical Kinetics in Microdroplets.

Micrometer-sized compartments play significant roles in driving heterogeneous transformations within atmospheric and biochemical systems as well as providing vehicles for drug delivery and novel reaction environments for the synthesis of industrial chemicals. Many reports now indicate that reaction kinetics are accelerated under microconfinement, for example, in sprays, thin films, droplets, aerosols, and emulsions. These observations are dramatic, posing a challenge to our understanding of chemical reaction mechanisms with potentially significant practical consequences for predicting the complex chemistry in natural systems. Here we introduce the idea of kinetic confinement, which is intended to provide a conceptual backdrop for understanding when and why microdroplet reaction kinetics differ from their macroscale analogs. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 75 is April 2024. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Models

Understanding the dynamics of counseling conversations is an important task, yet it is a challenging NLP problem regardless of the recent advance of Transformer-based pre-trained language models. This paper proposes a systematic approach to examine the efficacy of domain knowledge and large language models (LLMs) in better representing conversations between a crisis counselor and a help seeker. We empirically show that state-of-the-art language models such as Transformer-based models and GPT models fail to predict the conversation outcome. To provide richer context to conversations, we incorporate human-annotated domain knowledge and LLM-generated features; simple integration of domain knowledge and LLM features improves the model performance by approximately 15%. We argue that both domain knowledge and LLM-generated features can be exploited to better characterize counseling conversations when they are used as an additional context to conversations.
Local Wellposedness of dispersive equations with quasi-periodic initial data

We prove unconditional local well-posedness in a space of quasi-periodic functions for dispersive equations of the form $$\partial_tu + Lu + \partial_x(u^{p+1})=0,$$ where $L$ is a multiplier operator with purely imaginary symbol which grows at most exponentially. The class of equations to which our method applies includes the generalized Korteweg-de Vries equation, the generalized Benjamin-Ono equation, and the derivative nonlinear Schr\"odinger equation. We also discuss well-posedness of some dispersive models which do not have a problematic derivative in the nonlinearity, namely, the nonlinear Schr\"odinger equation and the generalized Benjamin-Bona-Mahony equation, with quasi-periodic initial data. In this way, we recover and improve upon results from arXiv:1212.2674v3 [math.AP], arXiv:2110.11263v1 [math.AP] and arXiv:2201.02920v1 [math.AP] by shorter arguments.
Efficient Solvers for Wyner Common Information with Application to Multi-Modal Clustering

We propose two novel extensions of the Wyner common information optimization problem. Each relaxes one fundamental constraints in Wyner's formulation. The \textit{Variational Wyner Common Information} relaxes the matching constraint to the known distribution while imposing conditional independence to the feasible solution set. We derive a tight surrogate upper bound of the obtained unconstrained Lagrangian via the theory of variational inference, which can be minimized efficiently. Our solver caters to problems where conditional independence holds with significantly reduced computation complexity; On the other hand, the \textit{Bipartite Wyner Common Information} relaxes the conditional independence constraint whereas the matching condition is enforced on the feasible set. By leveraging the difference-of-convex structure of the formulated optimization problem, we show that our solver is resilient to conditional dependent sources. Both solvers are provably convergent (local stationary points), and empirically, they obtain more accurate solutions to Wyner's formulation with substantially less runtime. Moreover, them can be extended to unknown distribution settings by parameterizing the common randomness as a member of the exponential family of distributions. Our approaches apply to multi-modal clustering problems, where multiple modalities of observations come from the same cluster. Empirically, our solvers outperform the state-of-the-art multi-modal clustering algorithms with significantly improved performance.
Robust Training of Federated Models with Extremely Label Deficiency

Federated semi-supervised learning (FSSL) has emerged as a powerful paradigm for collaboratively training machine learning models using distributed data with label deficiency. Advanced FSSL methods predominantly focus on training a single model on each client. However, this approach could lead to a discrepancy between the objective functions of labeled and unlabeled data, resulting in gradient conflicts. To alleviate gradient conflict, we propose a novel twin-model paradigm, called Twin-sight, designed to enhance mutual guidance by providing insights from different perspectives of labeled and unlabeled data. In particular, Twin-sight concurrently trains a supervised model with a supervised objective function while training an unsupervised model using an unsupervised objective function. To enhance the synergy between these two models, Twin-sight introduces a neighbourhood-preserving constraint, which encourages the preservation of the neighbourhood relationship among data features extracted by both models. Our comprehensive experiments on four benchmark datasets provide substantial evidence that Twin-sight can significantly outperform state-of-the-art methods across various experimental settings, demonstrating the efficacy of the proposed Twin-sight.
Balanced Resonate-and-Fire Neurons

The resonate-and-fire (RF) neuron, introduced over two decades ago, is a simple, efficient, yet biologically plausible spiking neuron model, which can extract frequency patterns within the time domain due to its resonating membrane dynamics. However, previous RF formulations suffer from intrinsic shortcomings that limit effective learning and prevent exploiting the principled advantage of RF neurons. Here, we introduce the balanced RF (BRF) neuron, which alleviates some of the intrinsic limitations of vanilla RF neurons and demonstrates its effectiveness within recurrent spiking neural networks (RSNNs) on various sequence learning tasks. We show that networks of BRF neurons achieve overall higher task performance, produce only a fraction of the spikes, and require significantly fewer parameters as compared to modern RSNNs. Moreover, BRF-RSNN consistently provide much faster and more stable training convergence, even when bridging many hundreds of time steps during backpropagation through time (BPTT). These results underscore that our BRF-RSNN is a strong candidate for future large-scale RSNN architectures, further lines of research in SNN methodology, and more efficient hardware implementations.
Grid Minors and Products

Motivated by recent developments regarding the product structure of planar graphs, we study relationships between treewidth, grid minors, and graph products. We show that the Cartesian product of any two connected $n$-vertex graphs contains an $\Omega(\sqrt{n})\times\Omega(\sqrt{n})$ grid minor. This result is tight: The lexicographic product (which includes the Cartesian product as a subgraph) of a star and any $n$-vertex tree has no $\omega(\sqrt{n})\times\omega(\sqrt{n})$ grid minor.
NeuroFlux: Memory-Efficient CNN Training Using Adaptive Local Learning

Efficient on-device Convolutional Neural Network (CNN) training in resource-constrained mobile and edge environments is an open challenge. Backpropagation is the standard approach adopted, but it is GPU memory intensive due to its strong inter-layer dependencies that demand intermediate activations across the entire CNN model to be retained in GPU memory. This necessitates smaller batch sizes to make training possible within the available GPU memory budget, but in turn, results in substantially high and impractical training time. We introduce NeuroFlux, a novel CNN training system tailored for memory-constrained scenarios. We develop two novel opportunities: firstly, adaptive auxiliary networks that employ a variable number of filters to reduce GPU memory usage, and secondly, block-specific adaptive batch sizes, which not only cater to the GPU memory constraints but also accelerate the training process. NeuroFlux segments a CNN into blocks based on GPU memory usage and further attaches an auxiliary network to each layer in these blocks. This disrupts the typical layer dependencies under a new training paradigm - $\textit{`adaptive local learning'}$. Moreover, NeuroFlux adeptly caches intermediate activations, eliminating redundant forward passes over previously trained blocks, further accelerating the training process. The results are twofold when compared to Backpropagation: on various hardware platforms, NeuroFlux demonstrates training speed-ups of 2.3$\times$ to 6.1$\times$ under stringent GPU memory budgets, and NeuroFlux generates streamlined models that have 10.9$\times$ to 29.4$\times$ fewer parameters.