Miracle-dz/UniFAR-FTUs huggingface dataset:
Interaction Dimensionality Scales Up to Generate Bimodal Consumer-Resource Size-Ratio Distributions in Ecological Communities

Understanding constraints on consumer-resource body size-ratios is fundamentally important from both ecological and evolutionary perspectives. By analyzing data on 4685 consumer-resource interactions from nine ecological communities, we show that in spatially complex environments—where consumers can forage in both two (2D, e.g., benthic zones) and three (3D, e.g., pelagic zones) spatial dimensions—the resource-to-consumer body size-ratio distribution tends towards bimodality, with different median 2D and 3D peaks. Specifically, we find that median size-ratio in 3D is consistently smaller than in 2D both within and across communities. Furthermore, 2D and 3D size (not size-ratio) distributions within any community are generally indistinguishable statistically, indicating that the bimodality in size-ratios is not driven simply by a priori size-segregation of species (and therefore, interactions) by dimensionality, due to other factors. We develop theory that correctly predicts the direction and magnitude of these differences between 2D and 3D size-ratio distributions. Our theory suggests that community-level size-ratio bimodality emerges from the stronger scaling of consumption rate with size in 3D interactions than in 2D, which both, maximizes consumer fitness, and allows coexistence, across a larger range of size-ratios in 3D. We also find that consumer gape-limitation can amplify differences between 2D and 3D size-ratios, and that for either dimensionality, higher carrying capacity allows coexistence of a wider range of size-ratios. Our results reveal new and general insights into the size structure of ecological communities, and show that spatial complexity of the environment can have far reaching effects on community structure and dynamics across scales of organization.
An Unsupervised Framework for Comparing Graph Embeddings

Graph embedding is the transformation of vertices of a graph into set of vectors. A good embedding should capture the graph topology, vertex-to-vertex relationship and other relevant information about the graph, its subgraphs and vertices. If these objectives are achieved, an embedding is a meaningful, understandable and compressed representations of a network. Finally, vector operations are simpler and faster than comparable operations on graphs. The main challenge is that one needs to make sure that embeddings well describe the properties of the graphs. In particular, a decision has to be made on the embedding dimensionality which highly impacts the quality of an embedding. As a result, selecting the best embedding is a challenging task and very often requires domain experts. In this article, we propose a ‘divergence score’ that can be assigned to embeddings to help distinguish good ones from bad ones. This general framework provides a tool for an unsupervised graph embedding comparison. In order to achieve it, we needed to generalize the well-known Chung-Lu model to incorporate geometry which is an interesting result in its own right. In order to test our framework, we did a number of experiments with synthetic networks as well as real-world networks, and various embedding algorithms.
Distinct Responses of the Nitrogen-Fixing Marine Cyanobacterium Trichodesmium to a Thermally Variable Environment as a Function of Phosphorus Availability

Surface temperature in the ocean is projected to be elevated and more variable in the future, which will interact with other environmental changes like reduced nutrient supplies. To explore these multiple stressor relationships, we tested the influence of thermal variation on the key marine diazotrophic cyanobacterium Trichodesmium erythraeum GBRTRLI101 as a function of the limiting nutrient phosphorus (P). Two constant temperature treatments represented current winter (22°C) and summer (30°C) mean values. Three variable temperature treatments fluctuated around the constant control values: Mean 22°C, either ± 2°C or ± 4°C; and mean 30°C ± 2°C. Each thermal treatment was grown under both P-replete (10 μmol/L) and P-limiting conditions (0.2 μmol/L). Effects of thermal variability on Trichodesmium were mainly found in the two winter variable temperature treatments (22°C ± 2°C or ± 4°C). P availability affected growth and physiology in all treatments and had significant interactions with temperature. P-replete cultures had higher growth and nitrogen and carbon fixation rates in the 22°C constant control, than in the corresponding variable treatments. However, physiological rates were not different in the P-replete constant and variable treatments at 30°C. In contrast, in P-limited cultures an advantage of constant temperature over variable temperature was not apparent. Phosphorus use efficiencies (PUE, mol N or C fixed h-1 mol cellular P-1) for nitrogen and carbon fixation were significantly elevated under P-limited conditions, and increased with temperature from 22 to 30°C, implying a potential advantage in a future warmer, P-limited environment. Taken together, these results imply that future increasing temperature and greater thermal variability could have significant feedback interactions with the projected intensification of P-limitation of marine N2-fixing cyanobacteria.
Alone versus In-a-group

Recognition and analysis of human affect has been researched extensively within the field of computer science in the past two decades. However, most of the past research in automatic analysis of human affect has focused on the recognition of affect displayed by people in individual settings and little attention has been paid to the analysis of the affect expressed in group settings. In this article, we first analyze the affect expressed by each individual in terms of arousal and valence dimensions in both individual and group videos and then propose methods to recognize the contextual information, i.e., whether a person is alone or in-a-group by analyzing their face and body behavioral cues. For affect analysis, we first devise affect recognition models separately in individual and group videos and then introduce a cross-condition affect recognition model that is trained by combining the two different types of data. We conduct a set of experiments on two datasets that contain both individual and group videos. Our experiments show that (1) the proposed Volume Quantized Local Zernike Moments Fisher Vector outperforms other unimodal features in affect analysis; (2) the temporal learning model, Long-Short Term Memory Networks, works better than the static learning model, Support Vector Machine; (3) decision fusion helps to improve affect recognition, indicating that body behaviors carry emotional information that is complementary rather than redundant to the emotion content in facial behaviors; and (4) it is possible to predict the context, i.e., whether a person is alone or in-a-group, using their non-verbal behavioral cues.
Examining Untempered Social Media: Analyzing Cascades of Polarized Conversations

Online social media, periodically serves as a platform for cascading polarizing topics of conversation. The inherent community structure present in online social networks (ho-mophily) and the advent of fringe outlets like Gab have created online “echo chambers” that amplify the effects of polarization, which fuels detrimental behavior. Recently, in October 2018, Gab made headlines when it was revealed that Robert Bowers, the individual behind the Pittsburgh Synagogue massacre, was an active member of this social media site and used it to express his anti-Semitic views and discuss conspiracy theories. Thus to address the need of automated data-driven analyses of such fringe outlets, this research proposes novel methods to discover topics that are prevalent in Gab and how they cascade within the network. Specifically, using approximately 34 million posts, and 3.7 million cascading conversation threads with close to 300k users; we demonstrate that there are essentially five cascading patterns that manifest in Gab and the most “viral” ones begin with an echo-chamber pattern and grow out to the entire network. Also, we empirically show, through two models viz. Susceptible-Infected and Bass, how the cascades structurally evolve from one of the five patterns to the other based on the topic of the conversation with upto 84% accuracy.
Word-level Speech Recognition with a Dynamic Lexicon

We propose a direct-to-word sequence model with a dynamic lexicon. Our word network constructs word embeddings dynamically from the character level tokens. The word network can be integrated seamlessly with arbitrary sequence models including Connectionist Temporal Classification and encoder-decoder models with attention. Sub-word units are commonly used in speech recognition yet are generated without the use of acoustic context. We show our direct-to-word model can achieve word error rate gains over sub-word level models for speech recognition. Furthermore, we empirically validate that the word-level embeddings we learn contain significant acoustic information, making them more suitable for use in speech recognition. We also show that our direct-to-word approach retains the ability to predict words not seen at training time without any retraining.
Free intramuscular Mg2+ concentration calculated using both 31P and 1H NMRS‐based pH in the skeletal muscle of Duchenne muscular dystrophy patients

Early studies have demonstrated that (total) magnesium was decreased in skeletal muscle of Duchenne muscular dystrophy (DMD) patients. Free intramuscular Mg2+ can be derived from 31P NMRS measurements. The value of free intramuscular magnesium concentration ([Mg2+]) is highly dependent on precise knowledge of intracellular pH, which is abnormally alkaline in dystrophic muscle, possibly due to an expanded interstitial space, potentially causing an underestimation of [Mg2+]. We have recently shown that intracellular pH can be derived using 1H NMRS of carnosine.
Modeling Quantitative Value of Habitats for Marine and Estuarine Populations

Coastal habitats (e.g., seagrass beds, shallow mud and sand flats) strongly influence survival, growth, and reproduction of exploited marine fish and invertebrate species. Many of these species have declined over the past decades, coincident with widespread degradation of coastal habitats, such that an urgent need exists to model the quantitative value of coastal habitats to their population dynamics. Demand for habitat considerations will increase as fisheries management contends with habitat issues in stock assessments and management in general moves towards a more ecosystem-based approach. The modeling of habitat function to support fishery species has, to date, been done on a case-by-case basis involving diverse approaches and types of population models, which has made it difficult to generalize about methods for incorporating habitat into population models. In this review, we offer guiding concepts for how habitat effects can be incorporated in population models commonly used to simulate the population dynamics of exploited fish and invertebrate species. We categorize population models based on whether they are static or dynamic representations of population status, and for dynamic, further into unstructured, age/size class structured, and individual-based. We then use examples to illustrate how habitat has been incorporated, implicitly (correlative) and explicitly (mechanistically), into each of these categories. We describe the methods used and provide details on their implementation and utility to facilitate adaptation of the approaches for other species and systems. We anticipate that our review can serve as a stimulus for more widespread use of population models to quantify the value of coastal habitats for exploited species, so that their importance can be accurately realized and to facilitate cross-species and cross-system comparisons. Quantitative evaluation of habitat effects in population dynamics will increasingly be needed for traditional stock assessments, ecosystem-based fisheries management, conservation of at-risk habitats, and recovery of overexploited stocks that rely on critical coastal habitats during their life cycle.
Altruism in populations at the extinction transition

We study the evolution of cooperation as a birth-death process in spatially extended populations. The benefit from the altruistic behavior of a cooperator is implemented by decreasing the death rate of its direct neighbors. The cost of cooperation is the increase of a cooperator's death rate proportional to the number of its neighbors. When cooperation has higher cost than benefit, cooperators disappear. Then the dynamics reduces to the contact process with defectors as the single particle type. Increasing the benefit-cost ratio above 1, the extinction transition of the contact process splits into a set of nonequilibrium transitions between four regimes when increasing the baseline death rate $p$ as a control parameter: (i) defection only, (ii) coexistence, (iii) cooperation only, (iv) extinction. We investigate the transitions between these regimes. As the main result, we find that full cooperation is established at the extinction transition as long as benefit is strictly larger than cost. Qualitatively identical phase diagrams are obtained for populations on square lattices and in pair approximation. Spatial correlations with nearest neighbors only are thus sufficient for sustained cooperation.
Automated and Quantitative Assessment of Tactile Mislocalization After Stroke

Topesthesia, the recognition of tactile stimulation location on the skin, can be severely affected by neurological injuries, such as stroke. Despite topesthesia being crucial for manipulating objects and interacting with the environment during activities of daily living, deficits cannot be quantitatively captured with current clinical assessments and are, as a consequence, not well-understood. The present work describes a novel automated assessment tool for tactile mislocalization in neurological patients with somatosensory deficits. We present two cases of ischemic stroke patients, describe their tactile localization deficits with the automated assessment, and compare the results to a standard manual clinical assessment. Using the automated assessment tool, it was possible to identify, locate, precisely quantify, and depict the patients' deficits in topesthesia. In comparison, the clinical assessment was not sensitive enough and some deficits would remain undetected due to ceiling effects. In addition, an MRI structural analysis of the lesion supported the existence of somatosensory deficits. This novel and quantitative assessment may not only help to raise awareness of the implications of deficits in topesthesia, but would also allow monitoring recovery throughout the rehabilitation process, informing treatment design, and objectively evaluating treatment efficacy.
Minimal Interspecies Interaction Adjustment (MIIA): Inference of Neighbor-Dependent Interactions in Microbial Communities

An intriguing aspect in microbial communities is that pairwise interactions can be influenced by neighboring species. This creates context dependencies for microbial interactions that are based on the functional composition of the community. Context dependent interactions are ecologically important and clearly present in nature, yet firmly established theoretical methods are lacking from many modern computational investigations. Here, we propose a novel network inference method that enables predictions for interspecies interactions affected by shifts in community composition and species populations. Our approach first identifies interspecies interactions in binary communities, which is subsequently used as a basis to infer modulation in more complex multi-species communities based on the assumption that microbes minimize adjustments of pairwise interactions in response to neighbor species. We termed this rule-based inference minimal interspecies interaction adjustment (MIIA). Our critical assessment of MIIA has produced reliable predictions of shifting interspecies interactions that are dependent on the functional role of neighbor organisms. We also show how MIIA has been applied to a microbial community composed of competing soil bacteria to elucidate a new finding that – in many cases – adding fewer competitors could impose more significant impact on binary interactions. The ability to predict membership-dependent community behavior is expected to help deepen our understanding of how microbiomes are organized in nature and how they may be designed and/or controlled in the future.
MEA6 Deficiency Impairs Cerebellar Development and Motor Performance by Tethering Protein Trafficking

Meningioma expressed antigen 6 (MEA6), also called cutaneous T cell lymphoma-associated antigen 5 (cTAGE5), was initially found in tumor tissues. MEA6 is located in endoplasmic reticulum (ER) exit sites and regulates the transport of collagen, very low density lipoprotein, and insulin. It is also reported that MEA6 might be related to Fahr’s syndrome, which comprises neurological, movement, and neuropsychiatric disorders. Here, we show that MEA6 is critical to cerebellar development and motor performance. Mice with conditional knockout of MEA6 (Nestin-Cre;MEA6F/F) display smaller sizes of body and brain compared to control animals, and survive maximal 28 days after birth. Immunohistochemical and behavioral studies demonstrate that these mutant mice have defects in cerebellar development and motor performance. In contrast, PC deletion of MEA6 (pCP2-Cre;MEA6F/F) causes milder phenotypes in cerebellar morphology and motor behaviors. While pCP2-Cre;MEA6F/F mice have normal lobular formation and gait, they present the extensive self-crossing of PC dendrites and damaged motor learning. Interestingly, the expression of key molecules that participates in cerebellar development, including Slit2 and brain derived neurotrophic factor (BDNF), is significantly increased in ER, suggesting that MEA6 ablation impairs ER function and thus these proteins are arrested in ER. Our study provides insight into the roles of MEA6 in the brain and the pathogenesis of Fahr’s syndrome.
Beam Learning - Using Machine Learning for Finding Beam Directions

Beamforming is the key enabler for wireless communications in the mmWave bands. 802.11ad and WiGig are wireless technologies that currently use the 60 GHz unlicensed mmWave spectrum via beamforming techniques. It is likely that 5G systems will be considered for 60GHz unlicensed spectrum (apart from other unlicensed bands) deployments and hence must co-exist with 802.11ad and WiGig. 3GPP is taking steps towards achieving the same and the standardization for this is underway. The first step to achieve this co-existence is to find the interference-free directions, in other words identify the directions in which the nodes using these incumbent technologies are communicating and eliminate those directions from further communications. Such a mechanism can help to exploit the spatial holes rather than avoid communications even when only a few spatial directions are used by incumbents. Such a mechanism trivially increases the throughput of the proposed 5G systems. However, since the incumbent technologies may be unknown to the 5G mmWave nodes and their behavior may also be unknown apriori (for instance, parameters such as duty cycle, power levels, CSMA parameter used by 802.11ad are unknown to the 5G nodes), this spatial direction finding must be performed in a blind manner. In this paper, we use multi-armed bandits-based algorithms, a variant of machine learning algorithms, to blindly detect the beam directions (both along azimuth and elevation i.e., 3D-beamforming) used for communication by the incumbents. This work paves the way for combining the powerful of machine learning algorithms into 5G unlicensed mmWave systems. Numerical results show the superior performance of these algorithms over techniques that are commonly employed in such blind settings.
Characterization of Mucosa-Associated Microbiota in Matched Cancer and Non-neoplastic Mucosa From Patients With Colorectal Cancer

Colonic microbiota play important roles in the development of colorectal cancer. We aim to characterise the mucosa-associated microbiota in the tumour as well as the matched non-neoplastic mucosa from patients with colorectal cancer. Microbiota profiling in these samples was done using high-throughput 16S rRNA amplicon sequencing. Our results showed that the microbiota richness and diversity were similar between the tumour and non-neoplastic mucosae. Linear discriminant analysis effect size (LEfSe) analysis identified Fusobacterium and Campylobacter as the key genera of the tumour while Brevundimonas as the key genus of the non-neoplastic mucosa. In patients with shorter survival period, the relative abundance of Fusobacterium and Campylobacter was significantly higher in the tumour. Besides, regardless of the sites, tumour showed higher abundance of Fusobacterium. On the other hand, the relative abundance of Brevundimonas was significantly lower in the tumour. When validated with quantitative ddPCR, we found the absolute numbers of both Fusobacterium and F. nucleatum were significantly higher in the carcinoma from patients with shorter survival period, conventional type of adenocarcinoma in the distal portion of the large intestine (descending colon, sigmoidal colon, and rectum). In conclusion, our study showed a compositional alteration in the mucosa-associated microbiota in the tumour, which may contribute to the progression of colorectal cancer.
Latent Channel Networks

Latent Euclidean embedding models a given network by representing each node in a Euclidean space, where the probability of two nodes sharing an edge is a function of the distances between the nodes. This implies that for two nodes to share an edge with high probability, they must be relatively close in all dimensions. This constraint may be overly restrictive for describing modern networks, in which having similarities in at least one area may be sufficient for having a high edge probability. We introduce a new model, which we call Latent Channel Networks, which allows for such features of a network. We present an EM algorithm for fitting the model, for which the computational complexity is linear in the number of edges and number of channels and apply the algorithm to both synthetic and classic network datasets.
Building High-Quality Auction Fraud Dataset

Given the magnitude of online auction transactions, it is difficult to safeguard consumers from dishonest sellers, such as shill bidders. To date, the application of Machine Learning Techniques (MLTs) to auction fraud has been limited, unlike their applications for combatting other types of fraud. Shill Bidding (SB) is a severe auction fraud, which is driven by modern-day technologies and clever scammers. The difficulty of identifying the behavior of sophisticated fraudsters and the unavailability of training datasets hinder the research on SB detection. In this study, we developed a high-quality SB dataset. To do so, first, we crawled and preprocessed a large number of commercial auctions and bidders’ history as well. We thoroughly preprocessed both datasets to make them usable for the computation of the SB metrics. Nevertheless, this operation requires a deep understanding of the behavior of auctions and bidders. Second, we introduced two new SB patterns and implemented other existing SB patterns. Finally, we removed outliers to improve the quality of training SB data.