Miracle-dz/UniFAR-FTUs huggingface dataset:
Time resolved quantitative phosphoproteomics reveals distinct patterns of SHP2 dependence in EGFR signaling

SHP2 is a protein tyrosine phosphatase that normally potentiates intracellular signaling by growth factors, antigen receptors, and some cytokines; it is frequently mutated in childhood leukemias and other cancers. Here, we examine the role of SHP2 in the responses of breast cancer cells to EGF by monitoring phosphoproteome dynamics when SHP2 is allosterically inhibited by the small molecule SHP099. Data on phosphotyrosine abundance at more than 400 tyrosine residues reveals six distinct response signatures following SHP099 treatment and washout. These include putative substrate sites with increased phosphotyrosine abundance at early or late timepoints, and another class of sites that shows reduced phosphotyrosine abundance when SHP099 is present. Sites of decreased phospho-abundance are enriched on proteins with two nearby phosphotyrosine residues, which can be directly protected from dephosphorylation by the paired SH2 domains of SHP2 itself. These findings highlight how analysis of phosphoproteomic dynamics can provide insight into transmembrane signaling responses.
Semantic Representations and Query Langauages for Or-sets MS-CIS-92-88 LOGIC & COMPUTATION 53

Or-sets were introduced by Imielinski, Naqvi and Vadaparty for dealing with liimited forms of disjunctive information in database queries. Independently, Rounds used a similar notion for representing disjunctive and conjunctive information in the context of situation theory. In this paper we formulate a query language with adequate expressive power for or-sets. Using the notion of normalization of or-sets, queries at the "structural" and "conceptual" levels are distinguished. Losslessness of normalization is established for a large class of queries. We have obtained upper bounds for the cost of normalization. An approach related to that of rounds is used to provide semantics for or-sets. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-92-88. This technical report is available at ScholarlyCommons: http://repository.upenn. edu/cis_reports/469 Semantic Representations and Query Langauages for Or-sets MS-CIS-92-88 LOGIC & COMPUTATION 53 Leonid Libkin Limsoon Wong University of Pennsylvania School of Engineering and Applied Science Computer and Information Science Department
Maximizing Conjunctive Views in Deletion Propagation

In deletion propagation, tuples from the database are deleted in order to reflect the deletion of a tuple from the view. Such an operation may result in the (often necessary) deletion of additional tuples from the view, besides the intentionally deleted one. The article studies the complexity of deletion propagation, where the view is defined by a conjunctive query (CQ), and the goal is to maximize the number of tuples that remain in the view. Buneman et al. showed that for some simple CQs, this problem can be solved by a straightforward algorithm, which is called here the unidimensional algorithm. The article identifies additional cases of CQs where the unidimensional algorithm succeeds, and in contrast, shows that for some other CQs the problem is NP-hard to approximate better than some constant ratio. In fact, it is shown here that among the CQs without self joins, the hard CQs are exactly the ones that the unidimensional algorithm fails on. In other words, the following dichotomy result is proved: for every CQ without self joins, deletion propagation is either APX-hard or solvable (in polynomial time) by the unidimensional algorithm. The article then presents approximation algorithms for certain CQs where deletion propagation is APX-hard. Specifically, two constant-ratio (and polynomial-time) approximation algorithms are given for the class of sunflower CQs (i.e., CQs having a sunflower hypergraph) without self joins. The first algorithm, providing the approximation ratio 1 − 1/e, is obtained by formulating the problem at hand as that of maximizing a monotone submodular function subject to a matroid constraint, and then using a known algorithm for such maximization. The second algorithm gives a smaller approximation ratio, 1/2, yet in polynomial time even under combined complexity. Finally, it is shown that self joins can significantly harden approximation in deletion propagation.
Transducing Markov sequences

A Markov sequence is a basic statistical model representing uncertain sequential data, and it is used within a plethora of applications, including speech recognition, image processing, computational biology, radio-frequency identification (RFID), and information extraction. The problem of querying a Markov sequence is studied under the conventional semantics of querying a probabilistic database, where queries are formulated as finite-state transducers. Specifically, the complexity of two main problems is analyzed. The first problem is that of computing the confidence (probability) of an answer. The second is the enumeration of the answers in the order of decreasing confidence (with the generation of the top-k answers as a special case), or in an approximate order thereof. In particular, it is shown that enumeration in any subexponential-approximate order is generally intractable (even for some fixed transducers), and a matching upper bound is obtained through a proposed heuristic. Due to this hardness, a special consideration is given to restricted (yet common) classes of transducers that extract matches of a regular expression (subject to prefix and suffix constraints), and it is shown that these classes are, indeed, significantly more tractable.
It's High Time to Turn Off the High Lights!

Light pollution, a brand-new form of pollution in contemporary culture, has had adverse impacts on both people and wildlife. It's time to pay attention to the level of light pollution! In order to better solve those problems in a rational way, a myriad of mathematical methods was applied in this study thoroughly to assess the risk level of light as well as to provide ways for interventions to reduce pollution in various areas. A comprehensive evaluation model based on TOPSIS Model was well developed. The components that contribute to light pollution, which were categorized into the natural factors, human society, and the environmental quality were identified. They were all subdivided into secondary indicators. On the network, data about Chinese cities were gathered and processed. After calculating each index's weight by using Entropy Weight Method, the TOPSIS Model to determine the comprehensive level of each region by the composite score index was applied. A K-means clustering model to divide all outcomes into nine groups while taking the hierarchy of risk levels into account was further utilized. Based on population density and geographical features, Chengdu, Xichong, Yulin, and Gaoli Gong Mountains in China as an urban community were picked for a suburban community, a rural community, and a protected land location, respectively. Principal Component Analysis (PCA) to identify the key variables that affected light pollution was also implemented. These three core variables were: daytime duration, number of bird species and residential electricity intensity. The solutions' efficacy by sensitivity analysis was reasonably assessed. For sensitivity analysis, the urban community and suburban community as the research subjects were selected, and Excel to adjust the values to the direction of optimization by 10%, 20%, and 30%, respectively, were used. We finally came to the result: promotion of green buildings is most effective for both regions. Such a myriad of model could be applied to more occasions. This is an innovative and constructive evaluation method, which can be widely used in various evaluation problems in the future and help make contributions to various fields such as environment and humanity.
Who's next?: Integrating Non-Verbal Turn-Taking Cues for Embodied Conversational Agents

Taking turns in a conversation is a delicate interplay of various signals, which we as humans can easily decipher. Embodied conversational agents (ECAs) communicating with humans should leverage this ability for smooth and enjoyable conversations. Extensive research has analyzed human turn-taking cues, and attempts have been made to predict turn-taking based on observed cues. These cues vary from prosodic, semantic, and syntactic modulation over adapted gesture and gaze behavior to actively used respiration. However, when generating such behavior for social robots or ECAs, often only single modalities were considered, e.g., gazing. We strive to design a comprehensive system that produces cues for all non-verbal modalities: gestures, gaze, and breathing. The system provides valuable cues without requiring speech content adaptation. We evaluated our system in a VR-based user study with N = 32 participants executing two subsequent tasks. First, we asked them to listen to two ECAs taking turns in several conversations. Second, participants engaged in taking turns with one of the ECAs directly. We examined the system's usability and the perceived social presence of the ECAs' turn-taking behavior, both with respect to each individual non-verbal modality and their interplay. While we found effects of gesture manipulation in interactions with the ECAs, no effects on social presence were found.
Differential detection workflows for multi-sample single-cell RNA-seq data

In single-cell transcriptomics, differential gene expression (DE) analyses typically focus on testing differences in the average expression of genes between cell types or conditions of interest. Single-cell transcriptomics, however, also has the promise to prioritise genes for which the expression differ in other aspects of the distribution. Here we develop a workflow for assessing differential detection (DD), which tests for differences in the average fraction of samples or cells in which a gene is detected. After benchmarking eight different DD data analysis strategies, we provide a unified workflow for jointly assessing DE and DD. Using simulations and two case studies, we show that DE and DD analysis provide complementary information, both in terms of the individual genes they report and in the functional interpretation of those genes.
JASPER: fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression

Joint association analysis of multiple traits with multiple genetic variants can provide insight into genetic architecture and pleiotropy, improve trait prediction and increase power for detecting association. Furthermore, some traits are naturally high-dimensional, e.g., images, networks or longitudinally measured traits. Assessing significance for multitrait genetic association can be challenging, especially when the sample has population sub-structure and/or related individuals. Failure to adequately adjust for sample structure can lead to power loss and inflated type 1 error, and commonly used methods for assessing significance can work poorly with a large number of traits or be computationally slow. We developed JASPER, a fast, powerful, robust method for assessing significance of multitrait association with a set of genetic variants, in samples that have population sub-structure, admixture and/or relatedness. In simulations, JASPER has higher power, better type 1 error control, and faster computation than existing methods, with the power and speed advantage of JASPER increasing with the number of traits. JASPER is potentially applicable to a wide range of association testing applications, including for multiple disease traits, expression traits, image-derived traits and microbiome abundances. It allows for covariates, ascertainment and rare variants and is robust to phenotype model misspecification. We apply JASPER to analyze gene expression in the Framingham Heart Study, where, compared to alternative approaches, JASPER finds more significant associations, including several that indicate pleiotropic effects, some of which replicate previous results, while others have not previously been reported. Our results demonstrate the promise of JASPER for powerful multitrait analysis in structured samples.
Neural Interoceptive Processing is Modulated by Deep Brain Stimulation for Treatment Resistant Depression

Background A critical advance in depression research is to clarify the hypothesized role of interoceptive processing in neural mechanisms of treatment efficacy. This study tests whether cortical interoceptive processing, as indexed by the heartbeat-evoked potential (HEP), is modulated by deep brain stimulation (DBS) to the subcallosal cingulate (SCC) for treatment resistant depression (TRD). Methods Eight patients with TRD were enrolled in a study of SCC DBS safety and efficacy. Electroencephalography (EEG) and symptom severity measures were sampled in a laboratory setting over the course of a six-month treatment protocol. The primary outcome measure was an EEG-derived HEP, which reflects cortical processing of heartbeat sensation. Cluster-based permutation analyses were used to test the effect of stimulation and time in treatment on the HEP. The change in signal magnitude after treatment was correlated with change in depression severity as measured by the 17-item Hamilton Depression Rating Scale. Results HEP amplitude was greater after 24 weeks of treatment (t(7)=-4.40, p=. 003, g=-1.38, 95% Cl [-2.3, -0.42]), and this change was inversely correlated with latency of treatment response (rho = -0.75, 95% Cl [-0.95, -0.11], p=.03). An acute effect of DBS was also observed, but as a decrease in HEP amplitude (t(6) =6.66, p<. 001, g=2.19, 95% Cl [0.81, 3.54]). HEP differences were most pronounced over left posterior sensors from 405-425 ms post-stimulus. Conclusion Brain-based evidence substantiates a theorized link between interoception and depression, and suggests an interoceptive contribution to the mechanism of treatment efficacy with deep brain stimulation for severe depression.
Conversational Assessment of Mild Cognitive Impairment with Virtual Agents

Over 55 million adults worldwide have dementia, a syndrome characterized by deterioration in cognitive functioning. Screening for mild cognitive impairment is important to identify dementia early to facilitate diagnosis and initiate treatment that may modify the disease trajectory. However, standard cognitive screening tools are time-consuming, require expert administration, and make people feel as if they are being tested and are thus potentially stigmatizing. Consequently, standard cognitive screening tools are underutilized, and dementia is often identified much later in the disease process leading to greater health-related and societal harms. We explored cognitive ability assessments using virtual agents, in which assessments are made during conversational dialogues. We evaluated four strategies for the assessment of cognitive ability in a sample of 41 individuals with varying cognitive ability, comparing each strategy to scores on the Mini Mental Status Exam (MMSE), the gold standard clinical screening tool for cognitive impairment. One of the four conversational strategies demonstrated significant agreement with the MMSE, with a sensitivity of 92.3% and a specificity of 48.3% for identification of impairment compared to the MMSE. Participants reported moderate levels of satisfaction with the agent-based conversational assessment. This approach could be used to identify individuals warranting more evaluation for possible cognitive impairment.
Persistence of the primary somatosensory system in zebrafish

The somatosensory system detects peripheral stimuli that are translated into behaviors necessary for survival. Fishes and amphibians possess two somatosensory systems in the trunk: the primary somatosensory system, formed by the Rohon-Beard neurons, and the secondary somatosensory system, formed by the neural crest cell-derived neurons of the Dorsal Root Ganglia. Rohon-Beard neurons have been characterized as a transient population that mostly disappears during the first days of life and is functionally replaced by the Dorsal Root Ganglia. Here, I follow Rohon-Beard neurons in vivo and show that the entire repertoire remains present in zebrafish from 1-day post-fertilization until the juvenile stage, 15-days post-fertilization. These data indicate that zebrafish retain two complete somatosensory systems until at least up to a developmental stage when the animals display complex behavioral repertoires.
Gas-condensate flow modelling for shale gas reservoirs: MATLAB codes. [Dataset]

These codes are supplementary to the full thesis, which is available on OpenAIR: http://hdl.handle. net/10059/2144. The codes are provided in a single ZIP file, which will need to be unzipped to access the individual files. The dataset consists of 33 individual files, of which 3 are plain text (Library[.]) and the rest are MATLAB files in . m format. The abstract for the thesis reads as follows: This work investigates the gas Knudsen flow under condensate saturation effect and phase behaviour deviation under capillary pressure of gas-condensate fluids in shale matrix with pore size distribution; and evaluates their effect on well productivity.
Conformational sampling and interpolation using language-based protein folding neural networks

Protein language models (PLMs), such ESM2, learn a rich semantic grammar of the protein sequence space. When coupled to protein folding neural networks (e.g., ESMFold), they can facilitate the prediction of tertiary and quaternary protein structures at high accuracy. However, they are limited to modeling protein structures in single states. This manuscript demonstrates that ESMFold can predict alternate conformations of some proteins, including de novo designed proteins. Randomly masking the sequence prior to PLM input returned alternate embeddings that ESMFold sometimes mapped to distinct physiologically relevant conformations. From there, inversion of the ESMFold trunk facilitated the generation of high-confidence interconversion paths between the two states. These paths provide a deeper glimpse of how language-based protein folding neural networks derive structural information from high-dimensional sequence representations, while exposing limitations in their general understanding of protein structure and folding.
Balance™ methodology – converting carbon finance to biodiversity creation

ABSTRACT This paper addresses two interlinked problems in sustainable development and suggests a methodology to resolve them. The first is the reduction of atmospheric greenhouse gas emissions, especially carbon dioxide. The second is the maintenance of biodiversity. Current carbon financing and environmental stewardship mechanisms underwhelm, often diluting intended positive effects. Most existing carbon credits do not have protection after 40 years, placing projects substantially beneath the C02 radiative forcing cycle. This paper presents the ‘Balance’ approach to sustainable development, including contractual principles ensuring C02 reduction, biodiversity enhancement and financial accountability. We describe two novel measures: a carbon calculator for commercial entities, and a new metric, the Balance Unit, combining biodiversity creation with carbon credits. A case study, spanning over 20 years at the Forest of Marston Vale, is then presented. It finds an increase in tree cover, CO2 sequestration, reduction in agricultural GHG emissions, sulphur dioxide and particulate matter absorption, and annual local economic benefits totalling £UK12.83 million. Expository detail regarding the ‘Planting Principles’ practised at Marston Vale is also provided. We argue that the Balance methodology, especially the Balance Unit, enables greater measurement reliability and long-term efficacy for maintaining biodiversity and reducing GHG emissions than current carbon financing approaches.
Wolbachia infection negatively impacts Drosophila simulans heat tolerance in a strain- and trait-specific manner

The susceptibility of insects to rising temperatures has largely been measured by their ability to survive thermal extremes. However, until recently, the capacity for maternally inherited endosymbionts to influence insect heat tolerance has been overlooked. Further, the impact of heat on traits like fertility, which can decline at temperatures below the lethal thermal limit has largely been ignored. Here, we assess the impact of three Wolbachia strains (wRi, wAu, and wNo) on the survival and fertility of Drosophila simulans exposed to heat stress during development or as adults. The impact of Wolbachia infection on heat tolerance was generally small and trait/strain specific. Only the wNo infection significantly reduced survival and fertility of adult males after a heat shock. When exposed to a fluctuating heat stress during development, the wRi and wAu strains reduced egg-to-adult survival but only the wNo infection reduced male fertility. Wolbachia densities of all three strains decreased under developmental heat stress, but reductions occurred at temperatures above those that reduced fertility of the host. These findings reveal the complexity of endosymbiont-host-environment interactions and emphasise the necessity to account for endosymbionts and their effect on both survival and fertility when investigating the vulnerability of insects to climate change.
DDHD2 is necessary for activity-driven fatty acid fueling of nerve terminal function

HSP54, a hereditary spastic paraplegia associated with cognitive impairment, is caused by mutations in the neuron-specific triglyceride (TG) lipase DDHD2. Loss of DDHD2 function results in lipid accumulation in human brains1 and lipid droplets (LDs) in mouse neurons2. In metabolically demanding tissues, TG lipases generate a fatty acid (FA) flux from LDs to fuel mitochondrial ATP production, but neurons are considered unable to use fat as an energy source. Thus, the basis for cognitive impairment driven by DDHD2 loss remains enigmatic. To resolve this paradox, we took advantage of presynaptic sensitivity to metabolic perturbations to determine if FAs derived from LDs could power local β-oxidation to support synaptic functions and whether DDHD2 activity would be required in the process. We demonstrate that nerve terminals are enriched with DDHD2 and blocking its activity leads to presynaptic accumulation of LDs. Moreover, we show that FAs derived from axonal LDs enter mitochondria in an activity-dependent fashion and drive local mitochondrial ATP production allowing nerve terminals to sustain function in the complete absence of glucose. Our data demonstrate that neurons and their nerve terminals can make use of LDs during electrical activity to provide metabolic support when glucose is in short supply.