Miracle-dz/UniFAR-FTUs huggingface dataset:
ANALYSIS OF A BACTERIAL SERINE/THREONINE KINASE

RdoA is a bacterial protein kinase from Salmonella enterica serovar Typhimurium first noted for its regulation of dsbA expression in this organism. The crystal structure of RdoA’s homologue, YihE from Escherichia coli, revealed a basic bi-lobal kinase domain that is a hallmark of the eukaryotic Ser/Thr, Tyr protein kinase superfamily. YihE however, bears the greatest structural similarity to choline kinase and aminoglycoside 3’phosphotransferase [APH(3’)]-IIIa which are both atypical kinases. RdoA and YihE have demonstrated the capacity for autophosphorylation in vitro and the ability to phosphorylate myelin basic protein, however, the native kinase target protein has not been identified. Based on structural alignment with APH(3’)-IIIa, predictions were made of key residues involved in ATP binding and catalysis and five YihE mutants were generated. Both the wildtype and YihE mutants were cloned for expression as Nterminal histidine-tagged proteins. In the work presented here, these proteins have been overexpressed and purified for further study. Mutational analyses revealed that four of the five mutants had decreased kinase activity in comparison to the wildtype protein, thereby establishing the mutated residues as important for enzymatic activity. Several attempts were made to elucidate the substrate of RdoA/YihE, however, it remains unknown. Further investigation is necessary to identify its substrate(s) and to pinpoint its physiological significance. RdoA is a member of the Cpx regulon and its absence stimulates Cpx activation. Since the Cpx system is involved in regulating expression of cell surface appendages and is one of three envelope stress response systems, it is hypothesized that RdoA serves to relay Cpx activation signals. This is supported by
Nor Gloom of Night: A New Conceptual Model for the Hubbard Brook Ecosystem Study

Abstract The great challenge now facing forest ecosystem scientists and managers is to address the need for multiple ecosystem services over relatively large spatial and temporal scales (e.g., whole national forests over 50- to 100-year time frames). Here we present a new conceptual model for the study of forest ecosystems that aids in the analysis of factors that influence ecosystem structure, function, and services. We then go on to show how this model has been applied to the long-term Hubbard Brook Ecosystem Study. Our new model has three main components: (1) controllers, (2) ecosystem pattern and process, and (3) ecosystem functions and services. The controllers are the factors that drive ecosystem pattern and process; we split them into two groups, state factors and variable–stochastic factors. This new model will help to ensure a comprehensive approach to forest ecosystem analysis and will facilitate interactions of research with policy and management at many locations.
RFID Radio Circuit Design in CMOS

This paper describes a reference design with circuits and an antenna for a UHF RFID system. A new Class 2 (Gen 2) tag was designed, including the extremely low-power integrated circuit integrated with an inexpensive printed antenna. Details on the design of the rectiﬁ er power regulator, demodulator, reset circuit, modulator, and antenna are highlighted. The paper includes a system-level software veriﬁ cation of the RFID tag system to determine proper voltage regulation and signal demodulation.
Social and Psychological factors for Superstition: A Brief Literature Review

: The present literature review focused on social and psychological factors associated with superstition. The literature included conceptual definition of superstition, prevalence and its probable underlying factors. It also sheds light upon the pros and cons for adherence with superstitious beliefs. Number of online articles published in peer reviewed journals, book reviews, abstracts, conference proceedings and dissertations were reviewed with a specific focus for psychosocial effects of superstition. The findings revealed that superstitious beliefs are widely spread and there are socio- learning predispositions which lie at backdrop of superstition. Superstition casts negative and positive influences on psychological health of individuals. Overall the paper is a worthy contribution for articulated understanding of phenomenon of superstition and its associated factors which opens horizons for further related and in depth inquiries. It explored different scientific and conceptual definition of superstition. Superstitions are appraisals for behaviors which involve lack of careful cognitive functions Game theory supposed the idea that Superstition are not erroneous acts rather some superstitious behaviors influence by rational suppositions significance of Friday 13 th for stock marketing (Kolb 1987; 2001. Demographic correlates, psychological and social factors linked with superstition behaviors explored in research articles were also analyzed. Findings suggested that Superstition contributes to overall improved psychological heath of individuals
Enhanced electrotransformation of the ethanologen Zymomonas mobilis ZM4 with plasmids

The Zymomonas mobilis ZM4 strain with excellent ethanol‐producing capabilities was the first strain of Z. mobilis, which was sequenced. This strain is resistant to transformation, and no previous study has shown a detailed protocol for electrotransfer of ZM4 with foreign DNA. In this work, many electrical and biological parameters were selected and evaluated in order to optimize the electrotransformation of ZM4. First, improved transformation efficiencies of 11 896, 99, 96 and 5989 transformants/μg DNA were separately achieved with shuttle plasmid pZB21‐mini (3082 bp), pZB21 (5930 bp), pZA22 (6994 bp) and broad‐host‐range vector pBBR1MCS‐2 (5144 bp) all prepared from Escherichia coli JM110. The crucial factors affecting the transformation efficiency included the source of the plasmid (the best strain was ZM4), origin and size of the plasmids, growth phase of the cells (the most ideal phase was early log phase with OD600 of 0.3–0.4), the electric field strength (generally 11.75 kV/cm–13.25 kV/cm) and the recovery time (3–24 h). Further, based upon the optimal transformation protocol mentioned above for replicative plasmids in ZM4, (i) the electrotransformation by recombinant plasmid pBBR1MCS‐2‐Pgap‐FLP (6880 bp) was an immediate success with the transformation efficiency 102 transformants/μg DNA; (ii) the site‐specific integration efficiencies (expressed in terms of “per μg of DNA”) of 3–6 integrating transformants was obtained using the integrating plasmid pBR328‐ldhR‐cml‐ldhL (7447 bp). This study will assist genetic and biotechnological research of ZM4 and other Z. mobilis strains by providing information about suitable vectors and a more universal and reliable procedure for introducing DNA into this strain.
Tau conformers in FTLD-MAPT undergo liquid-liquid phase separation and perturb the nuclear envelope

Recent studies show that a single MAPT gene mutation can promote alternative tau misfolding pathways engendering divergent forms of frontotemporal dementia and that under conditions of molecular crowding, the repertoire of tau forms can include liquid-liquid phase separation (LLPS). We show here that following pathogenic seeding, tau condenses on the nuclear envelope (NE) and disrupts nuclear-cytoplasmic transport (NCT). Interestingly, NE fluorescent tau signals and small fluorescent inclusions behaved as demixed liquid droplets in living cells. Thioflavin S-positive intracellular aggregates were prevalent in tau-derived inclusions with a size bigger than 3 μm2, indicating that a threshold of critical mass in the liquid state condensation may drive liquid-solid phase transitions. Our findings indicate that tau undergoing LLPS is more toxic amongst a spectrum of alternative conformers; LLPS droplets on the NE that disrupt NCT serve to trigger cell death and can act as nurseries for fibrillar structures abundantly detected in end-stage disease.
Genetic effect on the regeneration of the calli of rice

Rice (Oryza sativa L) is the most important food crop of the developing world. It provides up to two thirds of calories for more than two billion people in Asia. Unfortunately, this crop is threatened by several biotic and abiotic stresses. The improvement of rice to these stresses is of great importance. In vitro selection of favourable somaclonal variants from callus culture is a supplementary tool to traditional breeding for production of stress-resistant plants in rice. The introduction of a given genotype in in vitro selection programme depends on its aptitude to in vitro culture, callus induction and definitely on plant regeneration.
Shedding Light on Mediators’ Decision-Making

Shedding Light on Mediators’ Decision-Making: The Conscious & Unconscious Mental Processing Determining Experts’ Automatic Intuitive Decisions In efforts to provide better clarity about and to improve mediators’ performance and the quality of their decisions during the mediation process, I review the conscious and unconscious mental activities underpinning their judgments. Special attention is given to decisions that are automatic and intuitive, which are believed to dominate the process of decision-making in complex, fast-paced and dynamic conditions such as the mediation context. I challenge the prevailing assumption that decision accuracy is impaired by rapid decision speed, and propose the conditions in which fast automatic decisions could be more controllable and skillful. My proposition integrates the role of complex domain schema, explicit and implicit learning, knowledge acquisition processes and the interplay between conscious and unconscious subsystems for yielding skillful automatic judgments. The article culminates with highlighting the importance of continuous engagement in repetitive learning through reflective endeavor for developing mediation expertise. Tzofnat Peleg-Baker Shedding Light on Mediators’ Decision-Making Conscious & Unconscious Mental Processing Determining Experts’ Automatic Intuitive Decisions Tzofnat Peleg-Baker SSRN: https://ssrn.com/abstract=2560556 TzofnatPB@Gmail.com Tzofnat@Camden.Rutgers. edu
Achieving Outcome Fairness in Machine Learning Models for Social Decision Problems

Effective complements to human judgment, artificial intelligence techniques have started to aid human decisions in complicated social decision problems across the world. Automated machine learning/deep learning(ML/DL) classification models, through quantitative modeling, have the potential to improve upon human decisions in a wide range of decision problems on social resource allocation such as Medicaid and Supplemental Nutrition Assistance Program(SNAP, commonly referred to as Food Stamps). However, given the limitations in ML/DL model design, these algorithms may fail to leverage various factors for decision making, resulting in improper decisions that allocate resources to individuals who may not be in the most need of such resource. In view of such an issue, we propose in this paper the strategy of fairgroups, based on the legal doctrine of disparate impact, to improve fairness in prediction outcomes. Experiments on various datasets demonstrate that our fairgroup construction method effectively boosts the fairness in automated decision making, while maintaining high prediction accuracy.
A Gamified Assessment Platform for Predicting the Risk of Dementia +Parkinson's disease (DPD) Co-Morbidity

Population aging is becoming an increasingly important issue around the world. As people live longer, they also tend to suffer from more challenging medical conditions. Currently, there is a lack of a holistic technology-powered solution for providing quality care at affordable cost to patients suffering from co-morbidity. In this paper, we demonstrate a novel AI-powered solution to provide early detection of the onset of Dementia + Parkinson's disease (DPD) co-morbidity, a condition which severely limits a senior's ability to live actively and independently. We investigate useful in-game behaviour markers which can support machine learning-based predictive analytics on seniors' risk of developing DPD co-morbidity.
Logic Constrained Pointer Networks for Interpretable Textual Similarity

Systematically discovering semantic relationships in text is an important and extensively studied area in Natural Language Processing, with various tasks such as entailment, semantic similarity, etc. Decomposability of sentence-level scores via subsequence alignments has been proposed as a way to make models more interpretable. We study the problem of aligning components of sentences leading to an interpretable model for semantic textual similarity. In this paper, we introduce a novel pointer network based model with a sentinel gating function to align constituent chunks, which are represented using BERT. We improve this base model with a loss function to equally penalize misalignments in both sentences, ensuring the alignments are bidirectional. Finally, to guide the network with structured external knowledge, we introduce first-order logic constraints based on ConceptNet and syntactic knowledge. The model achieves an F1 score of 97.73 and 96.32 on the benchmark SemEval datasets for the chunk alignment task, showing large improvements over the existing solutions. Source code is available at https://github.com/manishb89/interpretable_sentence_similarity
Single Molecule Imaging of Tid1/Rdh54, a Rad54 Homolog That Translocates on Duplex DNA and Can Disrupt

Tid1 protein is important forthe recombinational repair of double-stranded DNA breaksduring meiosis. Tid1 is a member of Swi2/Snf2 family of chro-matin remodeling proteins and shares homology with Rad54.MembersofthisfamilyhydrolyzeATPandpromote1)chroma-tin remodeling, 2) DNA topology alterations, and 3) displace-mentofproteinsfromDNA.Alloftheseactivitiesarepresumedto require translocation of the protein on DNA. Here we usesingle-molecule visualization to provide direct evidence for theabilityofTid1totranslocateonDNA.Tid1translocationisATP-dependent, and the velocities are broadly distributed, with theaveragebeing84 39basepairs/s.Translocationisprocessive,with the average molecule traveling 10,000 base pairs beforepausing or dissociating. Many molecules display simple mono-tonic unidirectional translocation, but the majority displaycomplex translocation behavior comprising intermittentpauses, direction reversals, and velocity changes. Finally, wedemonstrate that translocation by Tid1 on DNA can result indisruption of three-stranded DNA structures. The ability ofTid1 translocation to clear DNA of proteins and to migraterecombination intermediates may be of critical importance forDNA repair and chromosome dynamics.
SBAT: Video Captioning with Sparse Boundary-Aware Transformer

In this paper, we focus on the problem of applying the transformer structure to video captioning effectively. The vanilla transformer is proposed for uni-modal language generation task such as machine translation. However, video captioning is a multimodal learning problem, and the video features have much redundancy between different time steps. Based on these concerns, we propose a novel method called sparse boundary-aware transformer (SBAT) to reduce the redundancy in video representation. SBAT employs boundary-aware pooling operation for scores from multihead attention and selects diverse features from different scenarios. Also, SBAT includes a local correlation scheme to compensate for the local information loss brought by sparse operation. Based on SBAT, we further propose an aligned cross-modal encoding scheme to boost the multimodal interaction. Experimental results on two benchmark datasets show that SBAT outperforms the state-of-the-art methods under most of the metrics.
An End-to-End Optimal Trade Execution Framework based on Proximal Policy Optimization

In this article, we propose an end-to-end adaptive framework for optimal trade execution based on Proximal Policy Optimization (PPO). We use two methods to account for the time dependencies in the market data based on two different neural network architecture: 1) Long short-term memory (LSTM) networks, 2) Fully-connected networks (FCN) by stacking the most recent limit orderbook (LOB) information as model inputs. The proposed framework can make trade execution decisions based on level-2 limit order book (LOB) information such as bid/ask prices and volumes directly without manually designed attributes as in previous research. Furthermore, we use a sparse reward function, which gives the agent reward signals at the end of each episode as an indicator of its relative performances against the baseline model, rather than implementation shortfall (IS) or a shaped reward function. The experimental results have demonstrated advantages over IS and the shaped reward function in terms of performance and simplicity. The proposed framework has outperformed the industry commonly used baseline models such as TWAP, VWAP, and AC as well as several Deep Reinforcement Learning (DRL) models on most of the 14 US equities in our experiments.
Verifying Fault-Tolerance in Probabilistic Swarm Systems

We present a method for reasoning about fault-tolerance in unbounded robotic swarms. We introduce a novel semantics that accounts for the probabilistic nature of both the swarm and possible malfunctions, as well as the unbounded nature of swarm systems. We define and interpret a variant of probabilistic linear-time temporal logic on the resulting executions, including those arising from faulty behaviour by some of the agents in the swarm. We specify the decision problem of parameterised fault-tolerance, which concerns determining whether a probabilistic specification holds under possibly faulty behaviour. We outline a verification procedure that we implement and use to study a foraging protocol from swarm robotics, and report the experimental results obtained.
Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification

Gait-based person re-identification (Re-ID) is valuable for safety-critical applications, and using only 3D skeleton data to extract discriminative gait features for person Re-ID is an emerging open topic. Existing methods either adopt hand-crafted features or learn gait features by traditional supervised learning paradigms. Unlike previous methods, we for the first time propose a generic gait encoding approach that can utilize unlabeled skeleton data to learn gait representations in a self-supervised manner. Specifically, we first propose to introduce self-supervision by learning to reconstruct input skeleton sequences in reverse order, which facilitates learning richer high-level semantics and better gait representations. Second, inspired by the fact that motion's continuity endows temporally adjacent skeletons with higher correlations (“locality”), we propose a locality-aware attention mechanism that encourages learning larger attention weights for temporally adjacent skeletons when reconstructing current skeleton, so as to learn locality when encoding gait. Finally, we propose Attention-based Gait Encodings (AGEs), which are built using context vectors learned by locality-aware attention, as final gait representations. AGEs are directly utilized to realize effective person Re-ID. Our approach typically improves existing skeleton-based methods by 10-20% Rank-1 accuracy, and it achieves comparable or even superior performance to multi-modal methods with extra RGB or depth information.
Biased Opinion Dynamics: When the Devil is in the Details

We investigate opinion dynamics in multi-agent networks when there exists a bias toward one of two possible opinions; for example, reflecting a status quo vs a superior alternative. Starting with all agents sharing an initial opinion representing the status quo, the system evolves in steps. In each step, one agent selected uniformly at random adopts with some probability a the superior opinion, and with probability 1 - a it follows an underlying update rule to revise its opinion on the basis of those held by its neighbors. We analyze the convergence of the resulting process under two well-known update rules, namely majority and voter. The framework we propose exhibits a rich structure, with a nonobvious interplay between topology and underlying update rule. For example, for the voter rule we show that the speed of convergence bears no significant dependence on the underlying topology, whereas the picture changes completely under the majority rule, where network density negatively affects convergence. We believe that the model we propose is at the same time simple, rich, and modular, affording mathematical characterization of the interplay between bias, underlying opinion dynamics, and social structure in a unified setting.
Ethics, Prosperity, and Society: Moral Evaluation Using Virtue Ethics and Utilitarianism

Modelling ethics is critical to understanding and analysing social phenomena. However, prior literature either incorporates ethics into agent strategies or uses it for evaluation of agent behaviour. This work proposes a framework that models both, ethical decision making as well as evaluation using virtue ethics and utilitarianism. In an iteration, agents can use either the classical Continuous Prisoner's Dilemma or a new type of interaction called moral interaction, where agents donate or steal from other agents. We introduce moral interactions to model ethical decision making. We also propose a novel agent type, called virtue agent, parametrised by the agent's level of ethics. Virtue agents' decisions are based on moral evaluations of past interactions. Our simulations show that unethical agents make short term gains but are less prosperous in the long run. We find that in societies with positivity bias, unethical agents have high incentive to become ethical. The opposite is true of societies with negativity bias. We also evaluate the ethicality of existing strategies and compare them with those of virtue agents.
The influenza A virus nucleoprotein gene controls the induction of both subtype specific and cross-reactive cytotoxic T cells

Using genetically typed recombinant influenza A viruses that differ only in their genes for nucleoprotein, we have demonstrated that repeated stimulation in vitro of C57BL/6 spleen cells primed in vivo with E61-13-H17 (H3N2) virus results in the selection of a population of cytotoxic T lymphocytes (CTL) whose recognition of infected target cells maps to the gene for nucleoprotein of the 1968 virus. Influenza A viruses isolated between 1934 and 1979 fall into two groups defined by their ability to sensitize target cells for lysis by these CTL: 1934- 1943 form one group (A/PR/8/34 related) and 1946-1979 form the second group (A/HK/8/68 related). These findings complement and extend our previous results with an isolated CTL clone with specificity for the 1934 nucleoprotein (27, 28). It is also shown that the same spleen cells derived from mice primed with E61-13-H17 virus in vivo, but maintained in identical conditions by stimulation with X31 virus (which differs from the former only in the origin of its gene for NP) in vitro, results in the selection of CTL that cross-react on target cells infected with A/PR/8/1934 (H1N1) or A/Aichi/1968 (H3N2). These results show that the influenza A virus gene for NP can play a role in selecting CTL with different specificities and implicate the NP molecule as a candidate for a target structure recognized by both subtype-directed and cross-reactive influenza A-specific cytotoxic T cells.