Miracle-dz/UniFAR-FTUs huggingface dataset:
Computing Bi-Lipschitz Outlier Embeddings into the Line

The problem of computing a bi-Lipschitz embedding of a graphical metric into the line with minimum distortion has received a lot of attention. The best-known approximation algorithm computes an embedding with distortion $O(c^2)$, where $c$ denotes the optimal distortion [Bădoiu \etal~2005]. We present a bi-criteria approximation algorithm that extends the above results to the setting of \emph{outliers}. Specifically, we say that a metric space $(X,\rho)$ admits a $(k,c)$-embedding if there exists $K\subset X$, with $|K|=k$, such that $(X\setminus K, \rho)$ admits an embedding into the line with distortion at most $c$. Given $k\geq 0$, and a metric space that admits a $(k,c)$-embedding, for some $c\geq 1$, our algorithm computes a $({\mathsf p}{\mathsf o}{\mathsf l}{\mathsf y}(k, c, \log n), {\mathsf p}{\mathsf o}{\mathsf l}{\mathsf y}(c))$-embedding in polynomial time. This is the first algorithmic result for outlier bi-Lipschitz embeddings. Prior to our work, comparable outlier embeddings where known only for the case of additive distortion.
On a complete and sufficient statistic for the correlated Bernoulli random graph model

Inference on vertex-aligned graphs is of wide theoretical and practical importance. There are, however, few flexible and tractable statistical models for correlated graphs, and even fewer comprehensive approaches to parametric inference on data arising from such graphs. In this paper, we consider the correlated Bernoulli random graph model (allowing different Bernoulli coefficients and edge correlations for different pairs of vertices), and we introduce a new variance-reducing technique---called \emph{balancing}---that can refine estimators for model parameters. Specifically, we construct a disagreement statistic and show that it is complete and sufficient; balancing can be interpreted as Rao-Blackwellization with this disagreement statistic. We show that for unbiased estimators of functions of model parameters, balancing generates uniformly minimum variance unbiased estimators (UMVUEs). However, even when unbiased estimators for model parameters do {\em not} exist---which, as we prove, is the case with both the heterogeneity correlation and the total correlation parameters---balancing is still useful, and lowers mean squared error. In particular, we demonstrate how balancing can improve the efficiency of the alignment strength estimator for the total correlation, a parameter that plays a critical role in graph matchability and graph matching runtime complexity.
Cooperative Adaptive Learning Control for A Group of Nonholonomic UGVs by Output Feedback

A high-gain observer-based cooperative deterministic learning (CDL) control algorithm is proposed in this chapter for a group of identical unicycle-type unmanned ground vehicles (UGVs) to track over desired reference trajectories. For the vehicle states, the positions of the vehicles can be measured, while the velocities are estimated using the high-gain observer. For the trajectory tracking controller, the radial basis function (RBF) neural network (NN) is used to online estimate the unknown dynamics of the vehicle, and the NN weight convergence and estimation accuracy is guaranteed by CDL. The major challenge and novelty of this chapter is to track the reference trajectory using this observer-based CDL algorithm without the full knowledge of the vehicle state and vehicle model. In addition, any vehicle in the system is able to learn the knowledge of unmodeled dynamics along the union of trajectories experienced by all vehicle agents, such that the learned knowledge can be re-used to follow any reference trajectory defined in the learning phase. The learning-based tracking convergence and consensus learning results, as well as using learned knowledge for tracking experienced trajectories, are shown using the Lyapunov method. Simulation is given to show the effectiveness of this algorithm.
How research on language evolution contributes to linguistics

Abstract Since its inception in the second part of the 20th century, the science of language evolution has been exerting a growing and formative pressure on linguistics. More obviously, given its interdisciplinary character, the science of language evolution provides a platform on which linguists can meet and discuss a variety of problems pertaining to the nature of language and ways of investigating it with representatives of other disciplines and research traditions. It was largely in this way that the attention of linguists was attracted to the study of emerging sign languages and gestures, as well as to the resultant reflection on the way different modalities impact communicative systems that use them. But linguistics also benefits from the findings made by language evolution researchers in the context of their own research questions and methodologies. The most important of these findings come out of the experimental research on bootstrapping communication systems and the evolution of communicative structure, and from mass comparison studies that correlate linguists data with a wide range of environmental variables.
Intellectual Field, Networks, and Reputation Economy in Romania After 1989

Abstract In this article, I analyse the transformations of the Romanian post-communist intellectual elites, using as a case study the disputes in the cultural press in Romania from 2002 to 2004, disagreements that influenced the repositioning of the Romanian public intellectuals through ideological alignments. Those debates gave birth afterwards to a cohesive Conservative pole and to anti-conservative tendencies of diverse political orientations, which constitutes the origin of the current divisions of the intellectual space. The analysis combines the Bourdieusian perspective on the social field and the theory of social networks with the purpose to formulate a hypothesis concerning the competitions meant to produce and preserve the prestige of the status groups in the social space that generate conflicting ideological positions. It outlines an alternative form of reassessing the “reputation economy” outside the space of the commodity exchange economy, starting instead with symbolic exchanges. The study describes the social rationale behind status production, as a source of strategies for maintaining dominant positions in a social field.
Infants’ Learning of Rule-Based Visual Sequences Predicts Language Outcome at 2 Years

The ability to learn and generalize abstract rules from sensory input – i.e., Rule Learning (RL) – is seen as pivotal to language development, and specifically to the acquisition of the grammatical structure of language. Although many studies have shown that RL in infancy is operating across different perceptual domains, including vision, no studies have directly investigated the link between infants’ visual RL and later language acquisition. Here, we conducted a longitudinal study to investigate whether 7-month-olds’ ability to detect visual structural regularities predicts linguistic outcome at 2 years of age. At 7 months, infants were tested for their ability to extract and generalize ABB and ABA structures from sequences of visual shapes, and at 24 months their lexical and grammatical skills were assessed using the MacArthur-Bates CDI. Regression analyses showed that infants’ visual RL abilities selectively predicted early grammatical abilities, but not lexical abilities. These results may provide the first evidence that RL mechanisms are involved in language acquisition, and suggest that RL abilities may act as an early neurocognitive marker for language impairments.
Fish Macrophages Show Distinct Metabolic Signatures Upon Polarization

Macrophages play important roles in conditions ranging from host immune defense to tissue regeneration and polarize their functional phenotype accordingly. Next to differences in the use of L-arginine and the production of different cytokines, inflammatory M1 macrophages and anti-inflammatory M2 macrophages are also metabolically distinct. In mammals, M1 macrophages show metabolic reprogramming toward glycolysis, while M2 macrophages rely on oxidative phosphorylation to generate energy. The presence of polarized functional immune phenotypes conserved from mammals to fish led us to hypothesize that a similar metabolic reprogramming in polarized macrophages exists in carp. We studied mitochondrial function of M1 and M2 carp macrophages under basal and stressed conditions to determine oxidative capacity by real-time measurements of oxygen consumption and glycolytic capacity by measuring lactate-based acidification. In M1 macrophages, we found increased nitric oxide production and irg1 expression in addition to altered oxidative phosphorylation and glycolysis. In M2 macrophages, we found increased arginase activity, and both oxidative phosphorylation and glycolysis were similar to control macrophages. These results indicate that M1 and M2 carp macrophages show distinct metabolic signatures and indicate that metabolic reprogramming may occur in carp M1 macrophages. This immunometabolic reprogramming likely supports the inflammatory phenotype of polarized macrophages in teleost fish such as carp, similar to what has been shown in mammals.
A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints

This work presents a review and discussion of the challenges that must be solved in order to successfully develop swarms of Micro Air Vehicles (MAVs) for real world operations. From the discussion, we extract constraints and links that relate the local level MAV capabilities to the global operations of the swarm. These should be taken into account when designing swarm behaviors in order to maximize the utility of the group. At the lowest level, each MAV should operate safely. Robustness is often hailed as a pillar of swarm robotics, and a minimum level of local reliability is needed for it to propagate to the global level. An MAV must be capable of autonomous navigation within an environment with sufficient trustworthiness before the system can be scaled up. Once the operations of the single MAV are sufficiently secured for a task, the subsequent challenge is to allow the MAVs to sense one another within a neighborhood of interest. Relative localization of neighbors is a fundamental part of self-organizing robotic systems, enabling behaviors ranging from basic relative collision avoidance to higher level coordination. This ability, at times taken for granted, also must be sufficiently reliable. Moreover, herein lies a constraint: the design choice of the relative localization sensor has a direct link to the behaviors that the swarm can (and should) perform. Vision-based systems, for instance, force MAVs to fly within the field of view of their camera. Range or communication-based solutions, alternatively, provide omni-directional relative localization, yet can be victim to unobservable conditions under certain flight behaviors, such as parallel flight, and require constant relative excitation. At the swarm level, the final outcome is thus intrinsically influenced by the on-board abilities and sensors of the individual. The real-world behavior and operations of an MAV swarm intrinsically follow in a bottom-up fashion as a result of the local level limitations in cognition, relative knowledge, communication, power, and safety. Taking these local limitations into account when designing a global swarm behavior is key in order to take full advantage of the system, enabling local limitations to become true strengths of the swarm.
Cationic Arginine-Rich Peptides (CARPs): A Novel Class of Neuroprotective Agents With a Multimodal Mechanism of Action

There are virtually no clinically available neuroprotective drugs for the treatment of acute and chronic neurological disorders, hence there is an urgent need for the development of new neuroprotective molecules. Cationic arginine-rich peptides (CARPs) are an expanding and relatively novel class of compounds, which possess intrinsic neuroprotective properties. Intriguingly, CARPs possess a combination of biological properties unprecedented for a neuroprotective agent including the ability to traverse cell membranes and enter the CNS, antagonize calcium influx, target mitochondria, stabilize proteins, inhibit proteolytic enzymes, induce pro-survival signaling, scavenge toxic molecules, and reduce oxidative stress as well as, having a range of anti-inflammatory, analgesic, anti-microbial, and anti-cancer actions. CARPs have also been used as carrier molecules for the delivery of other putative neuroprotective agents across the blood-brain barrier and blood-spinal cord barrier. However, there is increasing evidence that the neuroprotective efficacy of many, if not all these other agents delivered using a cationic arginine-rich cell-penetrating peptide (CCPPs) carrier (e.g., TAT) may actually be mediated largely by the properties of the carrier molecule, with overall efficacy further enhanced according to the amino acid composition of the cargo peptide, in particular its arginine content. Therefore, in reviewing the neuroprotective mechanisms of action of CARPs we also consider studies using CCPPs fused to a putative neuroprotective peptide. We review the history of CARPs in neuroprotection and discuss in detail the intrinsic biological properties that may contribute to their cytoprotective effects and their usefulness as a broad-acting class of neuroprotective drugs.
Mechanisms of Leukemia Immune Evasion and Their Role in Relapse After Haploidentical Hematopoietic Cell Transplantation

Over the last decade, the development of multiple strategies to allow the safe transfer from the donor to the patient of high numbers of partially HLA-incompatible T cells has dramatically reduced the toxicities of haploidentical hematopoietic cell transplantation (haplo-HCT), but this was not accompanied by a similar positive impact on the incidence of post-transplantation relapse. In the present review, we will elaborate on how the unique interplay between HLA-mismatched immune system and malignancy that characterizes haplo-HCT may impact relapse biology, shaping the selection of disease variants that are resistant to the “graft-vs.-leukemia” effect. In particular, we will present current knowledge on genomic loss of HLA, a relapse modality first described in haplo-HCT and accounting for a significant proportion of relapses in this setting, and discuss other more recently identified mechanisms of post-transplantation immune evasion and relapse, including the transcriptional downregulation of HLA class II molecules and the enforcement of inhibitory checkpoints between T cells and leukemia. Ultimately, we will review the available treatment options for patients who relapse after haplo-HCT and discuss on how a deeper insight into relapse immunobiology might inform the rational and personalized selection of therapies to improve the largely unsatisfactory clinical outcome of relapsing patients.
Adipose Tissue Distribution, Inflammation and Its Metabolic Consequences, Including Diabetes and Cardiovascular Disease

Adipose tissue plays essential roles in maintaining lipid and glucose homeostasis. To date several types of adipose tissue have been identified, namely white, brown, and beige, that reside in various specific anatomical locations throughout the body. The cellular composition, secretome, and location of these adipose depots define their function in health and metabolic disease. In obesity, adipose tissue becomes dysfunctional, promoting a pro-inflammatory, hyperlipidemic and insulin resistant environment that contributes to type 2 diabetes mellitus (T2DM). Concurrently, similar features that result from adipose tissue dysfunction also promote cardiovascular disease (CVD) by mechanisms that can be augmented by T2DM. The mechanisms by which dysfunctional adipose tissue simultaneously promote T2DM and CVD, focusing on adipose tissue depot-specific adipokines, inflammatory profiles, and metabolism, will be the focus of this review. The impact that various T2DM and CVD treatment strategies have on adipose tissue function and body weight also will be discussed.
The Volatilomes of Symbiodiniaceae-Associated Bacteria Are Influenced by Chemicals Derived From Their Algal Partner

Biogenic volatile organic compounds (BVOCs) are a large group of molecules involved in trophic interactions, stress response and atmospheric chemistry. Although they have been extensively studied in terrestrial ecosystems, their identity and prevalence in the marine environment remains largely unexplored. Here we characterized the volatilome of two abundant marine bacteria that were previously identified as members of the core microbiome of Symbiodiniaceae (phylum: Dinoflagellata), the photosynthetic endosymbionts of reef building corals. To determine the influence of Symbiodiniaceae exudate on their associated bacteria, we incubated isolates of Marinobacter adhaerens HP15 and Labrenzia sp. 21p with Symbiodiniaceae culture filtrate or culture medium (control) and investigated their volatilomes using GC–MS. The volatilome of Labrenzia sp. incubated in Symbiodiniaceae filtrate was significantly different and more diverse relative to the control. In contrast, the overall composition of the M. adhaerens volatilomes were consistent between treatment and control. Among the 35 compounds detected in both bacterial species, the dominant chemical functional groups were halogenated hydrocarbons, aromatic hydrocarbons and organosulfurs, some of which are known to play roles in inter-organism signaling, to act as antioxidants and as antimicrobials. This study provides new insights into the potential sources and diversity of marine BVOCs, uncovering a wide range of molecules that may play important physiological and ecological roles for these organisms, while also revealing the role of Symbiodiniaceae-associated bacteria in the emission of important atmospheric gases.
Gut Microbiota Altered in Mild Cognitive Impairment Compared With Normal Cognition in Sporadic Parkinson's Disease

Background and Aim: Gut bacteria play an important role in the pathogenesis of Parkinson's disease (PD). However, the alteration of fecal microbiota in PD with cognitive impairment remains unexplored. This study aimed to explore whether the gut microbiota of patients with PD having mild cognitive impairment (PD-MCI) were different from those with PD having normal cognition (PD-NC) and from healthy controls (HC). Also, the study probed the association between altered gut microbiota and cognitive ability in patients with PD. Methods: The fecal bacteria composition and short-chain fatty acids of 13 patients with PD-MCI, 14 patients with PD-NC, and 13 healthy spouses were analyzed using 16S ribosomal RNA sequencing and gas chromatography–mass spectrometry. Results: Compared with HC, the fecal microbial diversities increased in patients with PD-MCI and PD-NC. After adjusting the influence of age, sex, body mass index, education, and constipation using the statistical method, the relative abundances of two families (Rikenellaceae and Ruminococcaceae) and four genera (Alistipes, Barnesiella, Butyricimonas, and Odoribacter) were found to be higher in the feces of the PD-MCI group compared with the other two groups. Moreover, the abundance of genus Blautia and Ruminococcus decreased obviously in the PD-MCI group compared with the PD-NC group. Further, the abundance of genera Butyricimonas, Barnesiella, Alistipes, Odoribacter, and Ruminococcus negatively correlated with cognition ability. Conclusion: Compared with HC and patients with PD-NC, the gut microbiota of patients with PD-MCI was significantly altered, particularly manifesting in enriched genera from Porphyromonadaceae family and decreased the abundance of genera Blautia and Ruminococcus.
Estrogens in Human Male Gonadotropin Secretion and Testicular Physiology From Infancy to Late Puberty

Several reports in humans as well as transgenic mouse models have shown that estrogens play an important role in male reproduction and fertility. Estrogen receptor alpha (ERα) and beta (ERβ) are expressed in different male tissues including the brain. The estradiol-binding protein GPER1 also mediates estrogen action in target tissues. In human testes a minimal ERα expression during prepuberty along with a marked pubertal up-regulation in germ cells has been reported. ERβ expression was detected mostly in spermatogonia, primary spermatocytes, and immature spermatids. In Sertoli cells ERβ expression increases with age. The aromatase enzyme (cP450arom), which converts androgens to estrogens, is widely expressed in human tissues (including gonads and hypothalamus), even during fetal life, suggesting that estrogens are also involved in human fetal physiology. Moreover, cP450arom is expressed in the early postnatal testicular Leydig cells and spermatogonia. Even though the aromatase complex is required for estrogen synthesis, its biological relevance is also related to the regulation of the balance between androgens and estrogens in different tissues. Knockout mouse models of aromatase (ArKO) and estrogen receptors (ERKOα, ERKOβ, and ERKOαβ) provide an important tool to study the effects of estrogens on the male reproductive physiology including the gonadal axis. High basal serum FSH levels were reported in adult aromatase-deficient men, suggesting that estrogens are involved in the negative regulatory gonadotropin feedback. However, normal serum gonadotropin levels were observed in an aromatase-deficient boy, suggesting a maturational pattern role of estrogen in the regulation of gonadotropin secretion. Nevertheless, the role of estrogens in primate testis development and function is controversial and poorly understood. This review addresses the role of estrogens in gonadotropin secretion and testicular physiology in male humans especially during childhood and puberty.
A Novel Long Non-coding RNA, MSTRG.51053.2 Regulates Cisplatin Resistance by Sponging the miR-432-5p in Non-small Cell Lung Cancer Cells

Objective: The aim of this study was to investigate the molecular mechanisms underlying cisplatin (DDP) resistance in non-small cell lung cancer (NSCLC) cells by constructing a competing endogenous RNA (ceRNA) network. Methods: The gene expression profiles of human lung adenocarcinoma DDP-resistant cell line (A549/DDP) and its progenitor (A549) were comparatively evaluated by whole-transcriptome sequencing. The differentially expressed genes (DEGs) were subjected to KEGG pathway analysis. The expression levels of mRNAs involved in several pathways associated with conferring DDP resistance to tumor cells were evaluated. The ceRNA network was constructed based on the mRNA expression levels and the sequencing data of miRNA and lncRNA. Several ceRNA regulatory relationships were validated. Results: We quantified the expression of 17 genes involved in the six pathways by quantitative real-time polymerase chain reaction (qRT-PCR). The differential protein expression levels of eight genes were quantified by western blotting. Western blot analysis revealed six differentially expressed proteins (MGST1, MGST3, ABCG2, FXYD2, ALDH3A1, and GST-ω1). Among the genes encoding these six proteins, ABCG2, ALDH3A1, MGST3, and FXYD2 exhibited interaction with 8 lncRNAs and 4 miRNAs in the ceRNA regulatory network. The expression levels of these lncRNAs and miRNAs were quantified in cells; among these, 6 lncRNAs and 4 miRNAs exhibited differential expression between A549/DDP and A549 groups, which were used to construct a ceRNA network. The ceRNA regulatory network of MSTRG51053.2-miR-432-5p-MGST3 was validated by luciferase reporter assay. Conclusion: The MSTRG51053.2 lncRNA may function as a ceRNA for miR-432-5p to regulate the DDP resistance in NSCLC. The MGST1, MGST3, GST-ω1, and ABCG2 mRNAs, miR-432-5p and miR-665 miRNAs, and MSTRG51053.2 and PPAN lncRNAs can serve as potential DDP drug targets to reverse DDP resistance in NSCLC.