Miracle-dz/UniFAR-FTUs huggingface dataset:
S. Enteritidis and S. Typhimurium Harboring SPI-1 and SPI-2 Are the Predominant Serotypes Associated With Human Salmonellosis in Saudi Arabia

Non-typhoidal Salmonella (NTS) strains are Gram negative bacterial pathogens that are associated with foodborne illness worldwide. During the process of infection, Salmonella uses two molecular injectisomes known as Type 3 Secretion Systems (T3SS) to secrete virulence factors that are encoded by Salmonella Pathogenicity Island-1 (SPI-1) and SPI-2 into host cells. These secretion systems play a major role in virulence, as shown in various animal models, but little is known about their role in human infections. In Saudi Arabia, NTS strains frequently cause human infections but data regarding these pathogenic strains is fairly limited. The aim of this study was to characterize Salmonella human clinical isolates in Riyadh, Saudi Arabia, by determining their serotype, testing for the presence of SPI-1 and SPI-2 genes and to determine the antibiotic resistance profiles of these strains. Using the rapid Check and Trace Salmonella™ (CTS) system our results demonstrate that S. Enteritidis and S. Typhimurium were the predominant serovars, followed by S. Livingstone, S. Kentucky and S. Poona among a list of 36 serovars reported for the first time in the country. In addition, SPI-1 genes were detected in 99% of the isolates, while the sifA gene (SPI-2) was not detected in 13.5% of the isolates. These results suggest that both the SPI-1 and SPI-2 virulence determinants are important for human infection. Moreover, we report the presence of a Multi-Drug (MDR) carbapenem resistant S. Kentucky isolate harboring the blaOXA−48 gene not reported previously in Saudi Arabia.
Degeneracy in sparse ERGMs with functions of degrees as sufficient statistics

A sufficient criterion for "non-degeneracy" is given for Exponential Random Graph Models on sparse graphs with sufficient statistics which are functions of the degree sequence. This criterion explains why statistics such as alternating $k$-star are non-degenerate, whereas subgraph counts are degenerate. It is further shown that this criterion is "almost" tight. Existence of consistent estimates is then proved for non-degenerate Exponential Random Graph Models.
Mitochondrial-Derived Peptides Are Down Regulated in Diabetes Subjects

Background: Mitochondrial dysfunction is implicated in the pathogenesis of Type 2 diabetes (T2D) and the development of diabetes related complications such as cardiovascular disease and stroke. Mitochondria produce several small polypeptides that may influence mitochondrial function and may impact on insulin sensitivity, such as humanin (HN) and the mitochondrial open reading frame of the 12S rRNA type-c (MOTS-c) that are mitochondrial derived proteins (MDP). The aim of this study was to determine MDP in normal, prediabetes and diabetes subjects. Subjects and Measurements: In this cross-sectional study, we analyzed the serum concentrations of MDP and adiponectin (ADP) in 225 subjects: normal (n = 68), pre-diabetes (n = 33), T2D less than (good control; n = 31), and greater than HbA1c 7% (poor control; n = 93) subjects. The relationship of serum MDP and ADP concentrations with biochemical and anthropometric measurements were performed and assessed by multilinear regression. Results: Serum HN concentrations were lower in T2D (p < 0.0001) and negatively correlated with age (p < 0.0001), HbA1c (p < 0.0001), glucose (p < 0.0001), triglycerides (p < 0.003), ALT (p < 0.004), and TG/HDL ratio (p < 0.001). Circulating HN levels were positively correlated to cholesterol (p < 0.017), LDL (p < 0.001), and HDL (p < 0.001). Linear regression analysis showed that HbA1c and ALT were two independent predictors of circulating HN. Similarly, serum MOTS-c was significantly lower in T2D subjects compared to controls (p < 0.007). Circulating MOTS-c positively correlated with BMI (p < 0.035), total cholesterol (p < 0.0001), and LDL (p < 0.001) and negatively correlated with age (p < 0.002), HbA1c (p < 0.001), and glucose (p < 0.002). Serum ADP concentrations were lower in T2D (p < 0.002) and negatively correlated with HbA1c (p < 0.001), weight (p < 0.032) TG (p < 0.0001), and ALT (p < 0.0001); and positively correlated with HDL (p < 0.0001) and HN (p < 0.003). Linear regression analysis showed that HbA1c and weight were two independent predictors of circulating ADP. Multilinear regression showed that HN and MOT-c correlated with each other, and only HN correlated with HbA1c. Conclusion: The MDPs HN and MOT-c, similar to ADP, are decreased in T2D and correlate with HbA1c. The data provide an additional evidence that mitochondrial dysfunction contributes to glycemic dysregulation and metabolic defects in T2D.
Caffeine consumption attenuates neurochemical modifications in the hippocampus of streptozotocin‐induced diabetic rats

Type 1 diabetes can affect hippocampal function triggering cognitive impairment through unknown mechanisms. Caffeine consumption prevents hippocampal degeneration and memory dysfunction upon different insults and is also known to affect peripheral glucose metabolism. Thus we now characterized glucose transport and the neurochemical profile in the hippocampus of streptozotocin‐induced diabetic rats using in vivo1H NMR spectroscopy and tested the effect of caffeine consumption thereupon. We found that hippocampal glucose content and transport were unaltered in diabetic rats, irrespective of caffeine consumption. However diabetic rats displayed alterations in their hippocampal neurochemical profile, which were normalized upon restoration of normoglycaemia, with the exception of myo‐inositol that remained increased (36 ± 5%, p < 0.01 compared to controls) likely reflecting osmolarity deregulation. Compared to controls, caffeine‐consuming diabetic rats displayed increased hippocampal levels of myo‐inositol (15 ± 5%, p < 0.05) and taurine (23 ± 4%, p < 0.01), supporting the ability of caffeine to control osmoregulation. Compared to controls, the hippocampus of diabetic rats displayed a reduced density of synaptic proteins syntaxin, synaptophysin and synaptosome‐associated protein of 25 kDa (in average 18 ± 1%, p < 0.05) as well increased glial fibrillary acidic protein (20 ± 5%, p < 0.05), suggesting synaptic degeneration and astrogliosis, which were prevented by caffeine consumption. In conclusion, neurochemical alterations in the hippocampus of diabetic rats are not related to defects of glucose transport but likely reflect osmoregulatory adaptations caused by hyperglycemia. Furthermore, caffeine consumption affected this neurochemical adaptation to high glucose levels, which may contribute to its potential neuroprotective effects, namely preventing synaptic degeneration and astrogliosis.
Intrinsic dimension of data representations in deep neural networks

Deep neural networks progressively transform their inputs across multiple processing layers. What are the geometrical properties of the representations learned by these networks? Here we study the intrinsic dimensionality (ID) of data-representations, i.e. the minimal number of parameters needed to describe a representation. We find that, in a trained network, the ID is orders of magnitude smaller than the number of units in each layer. Across layers, the ID first increases and then progressively decreases in the final layers. Remarkably, the ID of the last hidden layer predicts classification accuracy on the test set. These results can neither be found by linear dimensionality estimates (e.g., with principal component analysis), nor in representations that had been artificially linearized. They are neither found in untrained networks, nor in networks that are trained on randomized labels. This suggests that neural networks that can generalize are those that transform the data into low-dimensional, but not necessarily flat manifolds.
Natural Resource Ecology and Management 5-2019 Design Implications for Surveys to Monitor Monarch Butterfly Population Trends

The Iowa Multiple Species Inventory and Monitoring (MSIM) Program includes a protocol for monitoring butterfly density on conservation lands using transects. Most data are collected from sites chosen randomly; additional sites are chosen non-randomly for other reasons. We analyzed a 12-year dataset for monarchs to address how density (per 50 m2 transect section) responded to site selection (random vs. non-random), latitude, and measures of the amount of milkweed and canopy cover on survey transects. Between 2006 and 2017, we conducted 2,328 surveys on 420 sites and detected a total of 2,757 adult monarchs. Monarch densities peaked in 2010 for random sites and 2012 for non-random sites, but densities were lowest in 2013 for both site types. The density of monarchs at non-random transects (0.047, 95% CI = 0.031, 0.062) was higher than that at random transects (0.029, 95% CI = 0.019, 0.044) and the temporal trends of density for random and non-random sites were significantly different. Monarch density was positively correlated with UTM northing, suggesting that monarch density increases from south to north in Iowa. The percent of plots containing milkweed was positively correlated with monarch density whereas percent tree canopy cover was negatively correlated with monarch density. Our results show that non-random transects had more monarchs, which may be a concern when interpreting findings from some citizen science efforts that used nonprobabilistic sampling designs. Collectively, the MSIM program data provide a comprehensive assessment of monarch densities statewide as well as the first empirically-derived density estimates for monarchs on the breeding grounds and may prove helpful when refining future monitoring efforts.
The Relationship Between Reading Fluency and Arithmetic Fact Fluency and Their Shared Cognitive Skills: A Developmental Perspective

This study investigated the underlying cognitive abilities which are related to both fluency in reading and arithmetic across different developmental phases of their acquisition. An unselected sample of children in first (N = 83), second (N = 66), and third (N = 67) grades completed several reading and arithmetic fluency tasks, as well as rapid automatized naming (RAN), working memory (WM), and inhibition measures. The results of a stepwise regression analysis revealed differences in the predictive models of fluency in both academic domains in first grade. However, similar patterns were found in the second and third grades. Specifically, in first grade reading fluency was predicted by inhibition and WM, while arithmetic fact fluency was predicted by RAN and WM. In contrast, in second grade both types of fluency were predicted by RAN and WM, and in third grade only RAN was found to be a predictor. Alongside the gradual reduction in the cognitive components participating in reading and arithmetic fluency, the results of the present study suggest that both fluencies share the same underlying cognitive mechanisms. Practical implications of the current results are discussed.
What Makes Training Multi-Modal Networks Hard?

Consider end-to-end training of a multi-modal vs. a single-modal network on a task with multiple input modalities: the multi-modal network receives more information, so it should match or outperform its single-modal counterpart. In our experiments, however, we observe the opposite: the best single-modal network always outperforms the multi-modal network. This observation is consistent across different combinations of modalities and on different tasks and benchmarks. This paper identiﬁes two main causes for this performance drop: ﬁrst, multi-modal networks are often prone to overﬁtting due to increased capacity. Second, different modalities overﬁt and generalize at different rates, so training them jointly with a single optimization strategy is sub-optimal. We address these two problems with a technique we call Gradient Blending , which computes an optimal blend of modalities based on their overﬁtting behavior. We demonstrate that Gradient Blending outperforms widely-used baselines for avoiding overﬁtting and achieves state-of-the-art accuracy on various tasks including ﬁne-grained sport classiﬁcation, human action recognition, and acoustic event detection.
The abundance of massive compact galaxies at 1.0 < z < 3.0 in 3D-HST/CANDELS

Based on a large sample of massive (M* ⩾ 1010 M⊙) compact galaxies at 1.0 < z < 3.0 in five 3D-HST/CANDELS fields, we quantify the fractional abundance and comoving number density of massive compact galaxies as a function of redshift. The samples of compact quiescent galaxies (cQGs) and compact star-forming galaxies (cSFGs) are constructed by various selection criteria of compact galaxies in the literature, and the effect of compactness definition on abundance estimate has proven to be remarkable, particularly for the cQGs and cSFGs at high redshifts. Regardless of the compactness criteria adopted, their overall redshift evolutions of fractional abundance and number density are found to be rather similar. Large samples of the cQGs exhibit a sustained increase in number density from z ∼ 3 to 2 and a plateau at 1 < z < 2. For massive cSFGs, a plateau in the number density at 2 < z < 3 can be found, as well as a continuous drop from z ∼ 2 to 1. The evolutionary trends of the cQG and cSFG abundances support the scenario that the cSFGs at z ≳ 2 may have been rapidly quenched into quiescent phase via violent dissipational processes, such as major merger and disk instabilities. The rarity of the cSFGs at lower redshifts (z < 1) can be interpreted by the decrease of gas reservoirs in dark matter halos and the consequent low efficiency of gas-rich dissipation.
Revisiting the X-ray – Mass scaling relations of early-type galaxies with the mass of their globular cluster systems as a proxy for the total galaxy mass

Using globular cluster (GC) kinematics and photometry data, we calibrate the scaling relation between the total galaxy mass (M_TOT including dark matter) and total globular cluster system mass (M_GCS) in a sample of 30 early-type galaxies (ETG), confirming a nearly linear relationship between the two physical parameters. Using samples of 83 and 57 ETGs, we investigate this scaling relation in conjunction with the previously known relations between M_TOT and the ISM X-ray luminosity and temperature, respectively. We confirm that M_GCS can be effectively used as a proxy of M_TOT. We further find that the L_X,GAS - M_TOT relation is far tighter in the subsample of core ETGs, when compared to cusp ETGs. In core ETGs (old, passively evolving stellar systems) M_TOT is significantly larger than the total stellar mass M_STAR and the correlation with the hot gas properties is driven by their dark matter mass M_DM. Cusp ETGs have typically lower LX,GAS than core ETGs. In cusp ETGs, for a given M_DM, higher L_X,GAS is associated with higher M_STAR, suggesting stellar feedback as an important secondary factor for heating the ISM. Using the M_GCS- M_TOT scaling relations we compare 272 ETGs with previous estimates of the stellar-to-halo mass relation of galaxies. Our model-independent estimate of M_TOT results in a good agreement around halo masses of 10^12 Mo, but suggest higher star formation efficiency than usually assumed both at the low and at the high halo mass ends.
Airborne Prokaryote and Virus Abundance Over the Red Sea

Aeolian dust exerts a considerable influence on atmospheric and oceanic conditions negatively impacting human health, particularly in arid and semi-arid regions like Saudi Arabia. Aeolian dust is often characterized by its mineral and chemical composition; however, there is a microbiological component of natural aerosols that has received comparatively little attention. Moreover, the amount of materials suspended in the atmosphere is highly variable from day to day. Thus, understanding the variability of atmospheric dust loads and suspended microbes throughout the year is essential to clarify the possible effects of dust on the Red Sea ecosystem. Here, we present the first estimates of dust and microbial loads at a coastal site on the Red Sea over a 2-year period, supplemented with measurements from dust samples collected along the Red Sea basin in offshore waters. Weekly average dust loads from a coastal site on the Red Sea ranged from 4.6 to 646.11 μg m−3, while the abundance of airborne prokaryotic cells and viral-like particles (VLPs) ranged from 77,967 to 1,203,792 cells m−3 and from 69,615 to 3,104,758 particles m−3, respectively. To the best of our knowledge, these are the first estimates of airborne microbial abundance in this region. The elevated concentrations of resuspended dust particles and suspended microbes found in the air indicate that airborne microbes may potentially have a large impact on human health and on the Red Sea ecosystem.
Zebrafish Adjust Their Behavior in Response to an Interactive Robotic Predator

Zebrafish (Danio rerio) constitutes a valuable experimental species for the study of the biological determinants of emotional responses, such as fear and anxiety. Fear-related test paradigms traditionally entail the interaction between focal subjects and live predators, which may show inconsistent behavior throughout the experiment. To address this technical challenge, robotic stimuli are now frequently integrated in behavioral studies, yielding repeatable, customizable, and controllable experimental conditions. While most of the research has focused on open-loop control where robotic stimuli are preprogrammed to execute a priori known actions, recent work has explored the possibility of two-way interactions between robotic stimuli and live subjects. Here, we demonstrate a “closed-loop control” system to investigate fear response of zebrafish in which the response of the robotic stimulus is determined in real-time through a finite-state Markov chain constructed from independent observations on the interactions between zebrafish and their predator. Specifically, we designed a 3D-printed robotic replica of the zebrafish allopatric predator red tiger Oscar fish (Astronotus ocellatus), instrumented to interact in real-time with live subjects. We investigated the role of closed-loop control in modulating fear response in zebrafish through the analysis of the focal fish ethogram and the information-theoretic quantification of the interaction between the subject and the replica. Our results indicate that closed-loop control elicits consistent fear response in zebrafish and that zebrafish quickly adjust their behavior to avoid the predator's attacks. The augmented degree of interactivity afforded by the Markov-chain-dependent actuation of the replica constitutes a fundamental advancement in the study of animal-robot interactions and offers a new means for the development of experimental paradigms to study fear.
Galactic cosmic rays after the AMS-02 observations

The unprecedented quality of the data collected by the AMS-02 experiment onboard the International Space Station allowed us to address subtle questions concerning the origin and propagation of cosmic rays. Here we discuss the implications of these data for the injection spectrum of elements with different masses and for the diffusion coefficient probed by cosmic rays through their journey from the sources to the Earth. We find that the best fit to the spectra of primary and secondary nuclei requires (1) a break in the energy dependence of the diffusion coefficient at energies $\sim 300$ GV; (2) an injection spectrum that is the same for all nuclei heavier than helium, and different injections for both protons and helium. Moreover, if to force the injection spectrum of helium to be the same as for heavier nuclei, the fit to oxygen substantially worsens. Accounting for a small, $X_{s}\sim 0.4~\rm g~cm^{-2}$, grammage accumulated inside the sources leads to a somewhat better fit to the B/C ratio but makes the difference between He and other elements even more evident. The statistic and systematic error bars claimed by the AMS collaboration exceed the error that is expected from calculations once the uncertainties in the cross sections of production of secondary nuclei are taken into account. In order to make this point more quantitative, we present a novel parametrization of a large set of cross sections, relevant for cosmic ray physics, and we introduce the uncertainty in the branching ratios in a way that its effect can be easily grasped.
Rarely-switching linear bandits: optimization of causal effects for the real world

Excessively changing policies in many real world scenarios is difficult, unethical, or expensive. After all, doctor guidelines, tax codes, and price lists can only be reprinted so often. We may thus want to only change a policy when it is probable that the change is beneficial. In cases that a policy is a threshold on contextual variables we can estimate treatment effects for populations lying at the threshold. This allows for a schedule of incremental policy updates that let us optimize a policy while making few detrimental changes. Using this idea, and the theory of linear contextual bandits, we present a conservative policy updating procedure which updates a deterministic policy only when justified. We extend the theory of linear bandits to this rarely-switching case, proving that such procedures share the same regret, up to constant scaling, as the common LinUCB algorithm. However the algorithm makes far fewer changes to its policy and, of those changes, fewer are detrimental. We provide simulations and an analysis of an infant health well-being causal inference dataset, showing the algorithm efficiently learns a good policy with few changes. Our approach allows efficiently solving problems where changes are to be avoided, with potential applications in medicine, economics and beyond.
Multiple Causes: A Causal Graphical View

Unobserved confounding is a major hurdle for causal inference from observational data. Confounders---the variables that affect both the causes and the outcome---induce spurious non-causal correlations between the two. Wang & Blei (2018) lower this hurdle with "the blessings of multiple causes," where the correlation structure of multiple causes provides indirect evidence for unobserved confounding. They leverage these blessings with an algorithm, called the deconfounder, that uses probabilistic factor models to correct for the confounders. In this paper, we take a causal graphical view of the deconfounder. In a graph that encodes shared confounding, we show how the multiplicity of causes can help identify intervention distributions. We then justify the deconfounder, showing that it makes valid inferences of the intervention. Finally, we expand the class of graphs, and its theory, to those that include other confounders and selection variables. Our results expand the theory in Wang & Blei (2018), justify the deconfounder for causal graphs, and extend the settings where it can be used.