Miracle-dz/UniFAR-FTUs huggingface dataset:
Nitration and Glycation Turn Mature NGF into a Toxic Factor for Motor Neurons: A Role for p75NTR and RAGE Signaling in ALS.

INTRODUCTION Glycating stress can occur together with oxidative stress during neurodegeneration and contribute to the pathogenic mechanism. Nerve growth factor (NGF) accumulates in several neurodegenerative diseases. Besides promoting survival, NGF can paradoxically induce cell death by signaling through the p75 neurotrophin receptor (p75NTR). The ability of NGF to induce cell death is increased by nitration of its tyrosine residues under conditions associated with increased peroxynitrite formation. AIMS Here we investigated whether glycation also changes the ability of NGF to induce cell death and assessed the ability of post-translational modified NGF to signal through the receptor for advanced glycation end products (RAGEs). We also explored the potential role of RAGE-p75NTR interaction in the motor neuron death occurring in amyotrophic lateral sclerosis (ALS) models. RESULTS Glycation promoted NGF oligomerization and ultimately allowed the modified neurotrophin to signal through RAGE and p75NTR to induce motor neuron death at low physiological concentrations. A similar mechanism was observed for nitrated NGF. We provide evidence for the interaction of RAGE with p75NTR at the cell surface. Moreover, we observed that post-translational modified NGF was present in the spinal cord of an ALS mouse model. In addition, NGF signaling through RAGE and p75NTR was involved in astrocyte-mediated motor neuron toxicity, a pathogenic feature of ALS. INNOVATION Oxidative modifications occurring under stress conditions can enhance the ability of mature NGF to induce neuronal death at physiologically relevant concentrations, and RAGE is a new p75NTR coreceptor contributing to this pathway. CONCLUSION Our results indicate that NGF-RAGE/p75NTR signaling may be a therapeutic target in ALS. Antioxid. Redox Signal. 28, 1587-1602.
Parietal neurons encode expected gains in instrumental information

Significance We examine how the brain guides active sensing in awake, behaving primates using a paradigm in which information sampling is dissociated from reinforcement variables, such as cumulative future reward or reward prediction errors. We show that target selective cells in lateral intraparietal cortex encode decision variables based on expected gains in instrumental information—the extent to which a visual cue, when discriminated, is expected to reduce the uncertainty of a subsequent action. In natural behavior, animals have access to multiple sources of information, but only a few of these sources are relevant for learning and actions. Beyond choosing an appropriate action, making good decisions entails the ability to choose the relevant information, but fundamental questions remain about the brain’s information sampling policies. Recent studies described the neural correlates of seeking information about a reward, but it remains unknown whether, and how, neurons encode choices of instrumental information, in contexts in which the information guides subsequent actions. Here we show that parietal cortical neurons involved in oculomotor decisions encode, before an information sampling saccade, the reduction in uncertainty that the saccade is expected to bring for a subsequent action. These responses were distinct from the neurons’ visual and saccadic modulations and from signals of expected reward or reward prediction errors. Therefore, even in an instrumental context when information and reward gains are closely correlated, individual cells encode decision variables that are based on informational factors and can guide the active sampling of action-relevant cues.
Randomized, Open-Label Study of the Pharmacokinetics and Safety of Oral and Intravenous Administration of Omadacycline to Healthy Subjects

ABSTRACT Omadacycline is a first-in-class aminomethylcycline antibiotic with microbiological activity against Gram-positive and Gram-negative aerobes and anaerobes and atypical bacteria that is being developed for the treatment of acute bacterial skin and skin structure infections (ABSSSI) and community-acquired bacterial pneumonia (CABP). The bioavailability of a phase 3 tablet formulation relative to that obtained via intravenous (i.v.) administration (and of other oral formulations relative to that of the phase 3 tablet) was investigated in an open-label, randomized, four-period, crossover study with healthy subjects age 18 to 50 years. Subjects received omadacycline at 100 mg i.v. , 300 mg orally as two different tablet formulations with different dissolution profiles, and 300 mg as an oral solution. Plasma omadacycline concentrations were determined using a validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. Twenty of 24 subjects completed all treatment periods. The two tablet formulations produced equivalent total exposures. The phase 3 tablet produced an exposure equivalent to that of the 100-mg i.v. dose, with a geometric mean ratio (90% confidence intervals [CI]) for area under the concentration-time curve from 0 h to infinity [AUC∞]) of 1.00 (0.93, 1.07). The absolute bioavailability of the tablets was approximately 34.5%. Intersubject variability was consistent among the oral formulations (∼20 to 25%). Single oral and i.v. doses of omadacycline were well tolerated; three subjects experienced mild adverse events (dizziness, nausea, and vomiting) that resolved without intervention. A 300-mg dose of the tablet formulation of omadacycline intended for use in phase 3 studies produced a total exposure equivalent to that of a 100-mg i.v. dose.
Signalling requirements for Erwinia amylovora-induced disease resistance, callose deposition and cell growth in the non-host Arabidopsis thaliana.

Erwinia amylovora is the causal agent of the fire blight disease in some plants of the Rosaceae family. The non-host plant Arabidopsis serves as a powerful system for the dissection of mechanisms of resistance to E. amylovora. Although not yet known to mount gene-for-gene resistance to E. amylovora, we found that Arabidopsis activated strong defence signalling mediated by salicylic acid (SA), with kinetics and amplitude similar to that induced by the recognition of the bacterial effector avrRpm1 by the resistance protein RPM1. Genetic analysis further revealed that SA signalling, but not signalling mediated by ethylene (ET) and jasmonic acid (JA), is required for E. amylovora resistance. Erwinia amylovora induces massive callose deposition on infected leaves, which is independent of SA, ET and JA signalling and is necessary for E. amylovora resistance in Arabidopsis. We also observed tumour-like growths on E. amylovora-infected Arabidopsis leaves, which contain enlarged mesophyll cells with increased DNA content and are probably a result of endoreplication. The formation of such growths is largely independent of SA signalling and some E. amylovora effectors. Together, our data reveal signalling requirements for E. amylovora-induced disease resistance, callose deposition and cell fate change in the non-host plant Arabidopsis. Knowledge from this study could facilitate a better understanding of the mechanisms of host defence against E. amylovora and eventually improve host resistance to the pathogen.
Fusing Saliency Maps with Region Proposals for Unsupervised Object Localization

In this paper we address the problem of unsupervised localization of objects in single images. Compared to previous state-of-the-art method our method is fully unsupervised in the sense that there is no prior instance level or category level information about the image. Furthermore, we treat each image individually and do not rely on any neighboring image similarity. We employ deep-learning based generation of saliency maps and region proposals to tackle this problem. First salient regions in the image are determined using an encoder/decoder architecture. The resulting saliency map is matched with region proposals from a class agnostic region proposal network to roughly localize the candidate object regions. These regions are further refined based on the overlap and similarity ratios. Our experimental evaluations on a benchmark dataset show that the method gets close to current state-of-the-art methods in terms of localization accuracy even though these make use of multiple frames. Furthermore, we created a more challenging and realistic dataset with multiple object categories and varying viewpoint and illumination conditions for evaluating the method's performance in real world scenarios.
Search Time for a Small Ribbon and Application to Vesicular Release at Neuronal Synapses

The arrival of a Brownian particle at a narrow cusp located underneath a ball is a model of vesicular release at neuronal synapses, triggered by calcium ions. The asymptotic computation of the arrival time presents several difficulties that can be overcome using conformal mappings and asymptotic analysis of the model equations. Using a regular expansion of the solution of the Laplace equation in the mapped domain, we compute the solution involving both small and large spatial scales. We derive novel asymptotic formulas for Brownian escape through cusps in both two and three dimensions. The range of validity of the asymptotic formulas is challenged by stochastic simulations. Finally, we apply the analysis to estimate the vesicular release probability at presynaptic terminals and, in particular, we suggest that vesicular organization imposes a severe constraint on calcium channel localization: diffusing calcium ions can trigger vesicular release only in a specific range of positions that we provide.
A bounded multi-dimensional modal logic for autonomous cars based on local traffic and estimation

The decision-making module on an autonomous car is usually a periodic program. In every cycle, the program makes a decision such as acceleration, brake, initiating a lane change process or a turn process based on the current traffic information gathered from car sensors. In urban traffic with mixed type of vehicles, the real-time performance requirement is critical for the decision-making program while acquiring global knowledge of the traffic is less practical. In such an environment, communications between vehicles are unreliable and time-consuming, so it is often difficult to know the exact driving decisions of other cars in the next cycle. In order to guarantee safety, a feasible solution requires the reasonable estimation on the driving decisions of other cars in the near future. In this paper, we propose a BMML (Bounded Multi-dimensional Modal Logic) to specify the traffic situations with spatio-temproral properties taking account of the estimated evolvement on them in the near future. The logic contains a primitive spatial logic with navigation operators and estimation operators as modal operators. The satisfaction of a BMML formula depends on a snapshot of the current traffic condition and an estimation structure capturing the believed information on the driving decisions of other cars. Given a snapshot and an estimation structure, the satisfaction of a BMML formula can be determined with simple and deterministic reasoning, so it is feasible for taking a BMML formula as the guard condition of the decision-making program of an autonomous car. The usage of BMML is illustrated with a series of small examples.
A Variable Neighborhood Search for Flying Sidekick Traveling Salesman Problem

The efficiency and dynamism of Unmanned Aerial Vehicles (UAVs), or drones, present substantial application opportunities in several industries in the last years. Notably, the logistic companies gave close attention to these vehicles envisioning reduce delivery time and operational cost. A variant of the Traveling Salesman Problem (TSP) called Flying Sidekick Traveling Salesman Problem (FSTSP) was introduced involving drone-assisted parcel delivery. The drone is launched from the truck, proceeds to deliver parcels to a customer and then is recovered by the truck in a third location. While the drone travels through a trip, the truck delivers parcels to other customers as long as the drone has enough battery to hover waiting for the truck. This work proposes a hybrid heuristic that the initial solution is created from the optimal TSP solution reached by a TSP solver. Next, an implementation of the General Variable Neighborhood Search is used to obtain the delivery routes of truck and drone. Computational experiments show the potential of the algorithm to improve the delivery time significantly. Furthermore, we provide a new set of instances based on well-known TSPLIB instances.
Assessment and evaluation in interprofessional education: exploring the field.

BACKGROUND The practice of interprofessional education (IPE) is expanding rapidly in the United States and globally. The publication of competencies from the Interprofessional Education Collaborative (IPEC) was a significant step forward to recognize the importance of health professions collaboration and to guide institutions for educational program development. However, there remains substantial difficulty in implementation, as well as considerable variability in assessment of learners' interprofessional collaborative knowledge and skills and evaluation of IPE programs. METHODS We conducted a multi-methods project which included 20 key informant interviews, a literature review, and a meeting of an expert panel. Our goals were 1) explore the current field of IPE, 2) identify and disseminate best practices to institutions wishing to implement/augment IPE assessment and evaluation processes, 3) uncover gaps in current IPE assessment and evaluation practices, and 4) recommend next steps for the field. RESULTS A small and growing literature indicates evidence of the effectiveness of IPE. A diverse collection of methods and tools are used to assess and evaluate IPE learners and programs; these are often used without an explicit program-evaluation framework. CONCLUSIONS For the field to advance and to align with the demands of changing clinical care systems, robust assessment and evaluation methods, standardized use of common tools, and longitudinal assessment from diverse data streams are needed for IPE.
Trauma Coping Self-Efficacy: A Context-Specific Self-Efficacy Measure for Traumatic Stress.

OBJECTIVE The psychometric properties of a Trauma Coping Self-Efficacy (CSE-T) scale that assesses general trauma-related coping self-efficacy perceptions were assessed. METHOD Measurement equivalence was assessed using several different samples: hospitalized trauma patients (n₁ = 74, n₂ = 69, n₃ = 60), 3 samples of disaster survivors (n₁ = 273, n₂ = 227, n₃ = 138), and trauma-exposed college students (N = 242). This is the first multisample evaluation of the psychometric properties for a general trauma-related CSE measure. RESULTS Results showed that a brief and parsimonious 9-item version of the CSE performed well across the samples with a robust factor structure; factor structure and factor loadings were similar across study samples. DISCUSSION The 9-item scale CSE-T demonstrated measurement equivalence across samples indicating that the underlying concept of general posttraumatic CSE is organized in a similar manner in the different trauma-exposed groups. These results offer strong support for cross-event construct validity of the CSE-T scale. Associations of the CSE-T with important expected covariates showed significant evidence for convergent validity. Finally, discriminant validity was also supported. Replication of the factor structure, internal reliability, and other evidence for construct validity is a critical next step for future research. (PsycINFO Database Record
Ancient individuals from the North American Northwest Coast reveal 10,000 years of regional genetic continuity

Significance The peopling of the Americas has been examined on the continental level with the aid of SNP arrays, next generation sequencing, and advancements in ancient DNA, all of which have helped elucidate evolutionary histories. Regional paleogenomic studies, however, have received less attention and may reveal a more nuanced demographic history. We present genome-wide sequences of individuals from the northern Northwest Coast covering a timespan of ∼10,000 years and show that continental patterns of demography do not necessarily apply on the regional level. Compared with existing paleogenomic data, we show that geographically linked population samples from the Northwest Coast exhibit an early ancestral lineage and find that population structure existed among Native North American groups as early as the late Pleistocene. Recent genomic studies of both ancient and modern indigenous people of the Americas have shed light on the demographic processes involved during the first peopling. The Pacific Northwest Coast proves an intriguing focus for these studies because of its association with coastal migration models and genetic ancestral patterns that are difficult to reconcile with modern DNA alone. Here, we report the low-coverage genome sequence of an ancient individual known as “Shuká Káa” (“Man Ahead of Us”) recovered from the On Your Knees Cave (OYKC) in southeastern Alaska (archaeological site 49-PET-408). The human remains date to ∼10,300 calendar (cal) y B.P. We also analyze low-coverage genomes of three more recent individuals from the nearby coast of British Columbia dating from ∼6,075 to 1,750 cal y B.P. From the resulting time series of genetic data, we show that the Pacific Northwest Coast exhibits genetic continuity for at least the past 10,300 cal y B.P. We also infer that population structure existed in the late Pleistocene of North America with Shuká Káa on a different ancestral line compared with other North American individuals from the late Pleistocene or early Holocene (i.e., Anzick-1 and Kennewick Man). Despite regional shifts in mtDNA haplogroups, we conclude from individuals sampled through time that people of the northern Northwest Coast belong to an early genetic lineage that may stem from a late Pleistocene coastal migration into the Americas.
Computing Urban Traffic Congestions by Incorporating Sparse GPS Probe Data and Social Media Data

Estimating urban traffic conditions of an arterial network with GPS probe data is a practically important while substantially challenging problem, and has attracted increasing research interests recently. Although GPS probe data is becoming a ubiquitous data source for various traffic related applications currently, they are usually insufficient for fully estimating traffic conditions of a large arterial network due to the low sampling frequency. To explore other data sources for more effectively computing urban traffic conditions, we propose to collect various traffic events such as traffic accident and jam from social media as complementary information. In addition, to further explore other factors that might affect traffic conditions, we also extract rich auxiliary information including social events, road features, Point of Interest (POI), and weather. With the enriched traffic data and auxiliary information collected from different sources, we first study the traffic co-congestion pattern mining problem with the aim of discovering which road segments geographically close to each other are likely to co-occur traffic congestion. A search tree based approach is proposed to efficiently discover the co-congestion patterns. These patterns are then used to help estimate traffic congestions and detect anomalies in a transportation network. To fuse the multisourced data, we finally propose a coupled matrix and tensor factorization model named TCE_R to more accurately complete the sparse traffic congestion matrix by collaboratively factorizing it with other matrices and tensors formed by other data. We evaluate the proposed model on the arterial network of downtown Chicago with 1,257 road segments whose total length is nearly 700 miles. The results demonstrate the superior performance of TCE_R by comprehensive comparison with existing approaches.
Formation of nucleobases in a Miller–Urey reducing atmosphere

Significance The study shows that Miller–Urey experiments produce RNA nucleobases in discharges and laser-driven plasma impact simulations carried out in a simple prototype of reducing atmosphere containing ammonia and carbon monoxide. We carried out a self-standing description of chemistry relevant to hypothesis of abiotic synthesis of RNA nucleobases related to early-Earth chemical evolution under reducing conditions. The research addresses the chemistry of simple-model reducing atmosphere (NH3 + CO + H2O) and the role of formamide as an intermediate of nucleobase formation in Miller–Urey experiment. The explorations combine experiments performed using modern techniques of large, high-power shock wave plasma generation by hall terawatt lasers, electric discharges, and state-of-the-art ab initio free-energy calculations. The Miller–Urey experiments pioneered modern research on the molecular origins of life, but their actual relevance in this field was later questioned because the gas mixture used in their research is considered too reducing with respect to the most accepted hypotheses for the conditions on primordial Earth. In particular, the production of only amino acids has been taken as evidence of the limited relevance of the results. Here, we report an experimental work, combined with state-of-the-art computational methods, in which both electric discharge and laser-driven plasma impact simulations were carried out in a reducing atmosphere containing NH3 + CO. We show that RNA nucleobases are synthesized in these experiments, strongly supporting the possibility of the emergence of biologically relevant molecules in a reducing atmosphere. The reconstructed synthetic pathways indicate that small radicals and formamide play a crucial role, in agreement with a number of recent experimental and theoretical results.
Accuracy of Clinical Diagnosis of Dementia with Lewy Bodies versus Neuropathology.

BACKGROUND The first consensus criteria for dementia with Lewy bodies (DLB) published in 1996 were revised in 2005, partly because the original clinical criteria had suboptimal sensitivity. Few studies have assessed the accuracy of the 2005 criteria applied prospectively in newly diagnosed patients who have been followed longitudinally. OBJECTIVE To explore the correlation between clinical and pathological diagnoses in patients with DLB and Parkinson's disease with dementia (PDD). METHODS From a prospective referral cohort study with enriched recruitment of patients with DLB and PDD, we included the first 56 patients coming to autopsy. Patients had mild dementia at inclusion and were followed annually until death with standardized clinical assessments. Pathological assessment was performed blind to clinical information according to standardized protocols and consensus criteria for DLB. RESULTS 20 patients received a pathological diagnosis of Lewy body disease; the corresponding clinical diagnoses were probable DLB (n = 11), PDD (n = 5), probable (n = 2) or possible (n = 2) Alzheimer's disease (AD). Of 14 patients with a clinical diagnosis of probable DLB, 11 had DLB/PDD and 3 had AD at pathology. One patient with clinically possible DLB fulfilled criteria for pathological AD. Sensitivity, specificity, positive predictive value, and negative predictive values for probable DLB were 73%, 93%, 79%, and 90%. CONCLUSION Our findings suggest that the international clinical consensus criteria for DLB perform reasonably well. However, false positive and false negative diagnoses still occur, indicating that the criteria need to be improved, that biomarkers may be needed, and that neuropathological feedback is vital to improve accuracy.