Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanistic Insight into the Conformational Changes of Cas8 upon Binding to Different PAM Sequences in the Transposon-Encoded Type I-F CRISPR-Cas System

The INTEGRATE system is a gene-editing approach that offers advantages over the widely used CRISPR-Cas9 system. It does not introduce double strand breaks in the target DNA but rather integrates the desired DNA sequence directly into it. The first step in the integration process is PAM recognition, which is critical to understanding and optimizing the system. Experimental testing revealed varying integration efficiencies of different PAM mutants, and computational simulations were carried out to gain mechanistic insight into the conformational changes of Cas8 during PAM recognition. Our results showed that the interaction between Arg246 and Guanine at position (-1) of the target strand is critical for PAM recognition. We found that unfavorable interactions in the 5’-AC-3’ PAM mutant disrupted this interaction and may be responsible for its 0% integration efficiency. Additionally, we discovered that PAM sequences not only initiate the integration process but also regulate it through an allosteric mechanism that connects the N-terminal domain and the helical bundle of Cas8. This allosteric regulation was present in all PAMs tested, even those with lower integration efficiencies, such as 5’-TC-3’ and 5’-AC-3’. We identified the Cas8 residues that are involved in this regulation. Our findings provide valuable insights into PAM recognition mechanisms in the INTEGRATE system and can help improve the gene-editing technology.
A systematic analysis of the joint effects of ganglion cells, lagged LGN cells, and intercortical inhibition on spatiotemporal processing and direction selectivity

Simple cells in the visual cortex process spatial as well as temporal information of the visual stream and enable the perception of motion information. Previous work suggests that the direction selectivity of V1 simple cells is associated with a temporal offset in the thalamocortical input stream through lagged and non-lagged cells of the lateral geniculate nucleus (LGN), but also with intercortical inhibition. While there exists a large corpus of models for spatiotemporal receptive fields, the majority of them built-in the spatiotemporal dynamics by utilizing a combination of spatial and temporal functions and thus, do not explain the emergence of spatiotemporal dynamics on basis of network dynamics emerging in the retina and the LGN. In order to better comprehend the emergence of spatiotemporal processing and direction selectivity, we used a spiking neural network to implement the visual pathway from the retina to the primary visual cortex. By varying different functional parts in our network, we demonstrate how the direction selectivity of simple cells emerges through the interplay between two components: tuned intercortical inhibition and a temporal offset in the feedforward path through lagged LGN cells. Further, we observe that direction-selective simple cells are linked to a particular spiking pattern in a local excitatory-inhibitory circuit: If the stimulus moves in the non-preferred direction of a simple cell, inhibitory neurons with a different spatial position or tuning spike earlier, preventing the simple cell to spike. However, in the preferred direction, these inhibitory cells spike later, enabling the simple cell to spike. Author summary The origin of spatiotemporal receptive fields of layer 4 simple cells, and their selectivity for a certain moving direction of a stimulus has been investigated in a large corpus of model studies. The majority of these studies utilize a functional description to approximate the behavior of the previous areas along the visual pathway. Therefore, the emergence of spatiotemporal processing as a consequence of local neuronal dynamics in the preceding layers is not well understood. Here, we systematically analyze the different functional parts of a spiking neural network, resembling the early visual pathway, starting with retinal ganglion cells up to simple and inhibitory cells in layer 4. Our simulations show, how an initial spatiotemporal processing emerges by a delay in the ganglion surrounding field. Our work highlights the combined effect of tuned intercortical inhibition and lagged LGN cells for the direction selectivity of simple cells. While inhibition alone leads to a sharp orientation tuning, but to a bidirectional selectivity, lagged LGN cells implement a temporal offset in the thalamocortical input currents, giving one moving direction a temporal advantage, leading to an unidirectional selectivity.
Dynamics of the membrane- and cell wall-associated proteome of Arabidopsis thaliana roots in response to uranium stress

Uranium (U) is a non-essential and toxic metal for plants, which have the ability to take up uranyl ions from the soil and preferentially accumulate them in the roots. We showed that the bulk of the radionuclide accumulates in the root insoluble proteome of Arabidopsis plants challenged with U. Therefore, to elucidate new molecular mechanisms related to U stress response and tolerance, we used label-free quantitative proteomics to analyze the dynamics of the root membrane- and cell wall-enriched proteome under U stress. Of the 2,802 proteins identified, 458 showed differential accumulation in response to U. Biological processes affected by U include response to stress, amino acid metabolism, and previously unexplored functions associated with membranes and the cell wall. Indeed, our analysis supports a dynamic and complex reorganization of the cell wall in response to U stress, including lignin and suberin synthesis, pectin modifications, polysaccharide hydrolysis, and Casparian strips formation. Water flux through aquaporins and vesicular trafficking were also significantly perturbed by U stress. Finally, the abundance of metal transporters and iron, calcium, and other metal-binding proteins was affected by U. These proteins may play a role in controlling the fate and toxicity of U in plants.
Attractor-like circuits improve visual decoding and behavior in zebrafish

Attractor networks are neural circuits with stable states that represent information or memories. They play a crucial role in memory retrieval, decision-making and integrating noisy cues. In zebrafish larvae, the spontaneous dynamics of the optic tectum is structured according to topographically organized neuronal assemblies exhibiting attractor-like behavior. Here, we took advantage of the Methyl-CpG-binding protein 2 (MeCP2) deficient zebrafish mutant, which displays perturbed tectal dynamics, to study the functional role of the attractor-like circuits in visual processing. In comparison to wild-type larvae, the mecp2-mutant showed reduced functional connectivity in the optic tectum. This abnormal connectivity significantly affected the visual response, and the ability to discriminate between visual stimuli. Finally, the mutant larvae where less efficient in hunting paramecia. We argue that the attractor dynamics of the tectal assemblies improve stimulus discrimination, visual resolution, and increase the sensitivity to behaviorally relevant visual stimuli.
Coding for Channels With Rate-Limited Side Information at the Decoder, With Applications

In this work, coding for channel with partial state information at the decoder is studied. Specifically, the model under consideration assumes that the encoder is provided with full channel state information (CSI) in a noncausal manner, and the decoder is provided with partial state knowledge, quantified by a rate limit. Coding of side information intended for the channel decoder is a Wyner-Ziv like problem, since the channel output depends statistically on the state, thus serving as side information in retrieving the encoded state. Therefore, coding for such a channel involves the simultaneous solution of a Wyner-Ziv problem and a related Gel'fand-Pinsker problem. A single-letter characterization of the capacity of this channel is developed, that involves two rate constraints, in the form of Wyner-Ziv and Gel'fand-Pinsker formulas. Applications are suggested to watermarking problems, where a compressed version of the host signal is present at the decoder. Two main models are examined: the standard watermarking problem, where the decoder is interested only in the embedded information, and the reversible information embedding problem, where the decoder is interested also in exact reproduction of the host. For both problems, single-letter characterizations of the region of all achievable rate-distortion triples (R, Rd, D) are given, where R is the embedding rate, Rd is the rate limit of the compressed host at the decoder, and D is the average distortion between the host and the composite data set (stegotext). For reversible information embedding, two stages of attack are considered, modeled by a degraded broadcast channel, where the weaker (degraded) channel represents the second attack, and both decoders are required to fully reproduce the host.
A Probabilistic Constraint Approach for Robust Transmit Beamforming With Imperfect Channel Information

Transmit beamforming (or precoding) is a powerful technique for enhancing performance of wireless multiantenna communication systems. Standard transmit beamformers require perfect channel state information at the transmitter (CSIT) and are sensitive to errors in channel estimation. In practice, such errors are inevitable due to finite feedback resources, quantization errors and other physical constraints. Hence, robustness has become a crucial issue recently. Among two popular robust designs, the stochastic approach exploits channel statistics and optimizes the average system performance while the maximin approach considers errors as deterministic and optimizes the worst case performance. The latter usually leads to a very conservative design against extreme (but rare) conditions which may occur at a very low probability. In this paper, we propose a more flexible approach that maximizes the average signal-to-noise ratio (SNR) and takes the extreme conditions into account using the probability with which they may occur. Simulation results show that the proposed beamformer offers higher robustness against channel estimation errors than several popular transmit beamformers.
A Deterministic Single Exponential Time Algorithm for Most Lattice Problems based on Voronoi Cell Computations ( Extended Abstract )

We give deterministic 2-time algorithms to solve all the most important computational problems on point lattices in NP, including the Shortest Vector Problem (SVP), Closest Vector Problem (CVP), and Shortest Independent Vectors Problem (SIVP). This improves the n running time of the best previously known algorithms for CVP (Kannan, Math. Operation Research 12(3):415-440, 1987) and SIVP (Micciancio, Proc. of SODA, 2008), and gives a deterministic alternative to the 2-time (and space) randomized algorithm for SVP of (Ajtai, Kumar and Sivakumar, STOC 2001). The core of our algorithm is a new method to solve the closest vector problem with preprocessing (CVPP) that uses the Voronoi cell of the lattice (described as intersection of half-spaces) as the result of the preprocessing function. In the process, we also give algorithms for several other lattice problems, including computing the kissing number of a lattice, and computing the set of all Voronoi relevant vectors. All our algorithms are deterministic, and have 2 time and space complexity
New algorithms for skewing correction and slant removal on word-level [OCR]

The word preprocessing is a crucial stage of OCR systems. Here we present two algorithms appropriate for this stage. The first one corrects the skewing of words and the second removes the slant from handwritten words. Both algorithms make use of the Wigner-Ville distribution and the projection profile technique. The algorithms have been tested on words taken from more than 200 writers and the results obtained can be considered very satisfactory, since the overall accuracy of our OCR system is notably improved.
Consumer Surplus in the Digital Economy : Estimating the Value of Increased Product Variety at Online Booksellers Paper 176

We present a framework and empirical estimates that quantify the economic impact of increased product variety made available through electronic markets. While efficiency gains from increased competition significantly enhance consumer surplus, for instance by leading to lower average selling prices, our present research shows that increased product variety made available through electronic markets can be a significantly larger source of consumer surplus gains. One reason for increased product variety on the Internet is the ability of online retailers to catalog, recommend and provide a large number of products for sale. For example, the number of book titles available at Amazon.com is over 23 times larger than the number of books on the shelves of a typical Barnes & Noble superstore and 57 times greater than the number of books stocked in a typical large independent bookstore. Our analysis indicates that the increased product variety of online bookstores enhanced consumer welfare by $731 million to $1.03 billion in the year 2000, which is between seven to ten times as large as the consumer welfare gain from increased competition and lower prices in this market. There may also be large welfare gains in other SKU-intensive consumer goods such as music, movies, consumer electronics, and computer software and hardware. Acknowledgements: We thank Matthew Gentzkow, Austan Goolsbee, Jerry Hausman, Lorin Hitt, Guillermo Israilevich, participants at New York University, University of Arizona, University of California at Irvine, University of Maryland, University of Pittsburgh, the 2001 Workshop on Information Systems and Economics (WISE), the 2003 American Economic Association meetings, the 2003 International Industrial Organization Conference and the referees and editors of this journal for valuable comments on this paper. We thank Jeff Bezos, Al Greco, Vicki Jennings, Mary Ellen Keating, Jason Kilar, Steve Riggio, Madeline Schnapp, Jeff Wilke and representatives of the American Booksellers Association, the American Wholesale Booksellers Association, the Book Industry Study Group, Barnes and Noble, Amazon.com, MIT Press and various other publishers and booksellers for providing valuable information on the book industry. Generous financial support was provided by the Center for eBusiness at MIT under a grant from Fleet Bank and the National Science Foundation (grant number IIS-0085725). * Sloan School of Management, Massachusetts Institute of Technology; email: erikb@mit.edu; web: http://ebusiness.mit.edu/erik ** H. John Heinz III School of Public Policy and Management, Carnegie Mellon University; email: mds@cmu.edu; web: http://www.heinz. cmu.edu/~mds *** Sloan School of Management, Massachusetts Institute of Technology; email: yuhu@mit.edu
A Compiler Framework for Optimization of Affine Loop Nests for GPGPUs

GPUs are a class of specialized parallel architectures with tremendous computational power. The new Compute Unified Device Architecture (CUDA) programming model from NVIDIA facilitates programming of general purpose applications on their GPUs. However, manual development of high-performance parallel code for GPUs is still very challenging. In this paper, a number of issues are addressed towards the goal of developing a compiler framework for automatic parallelization and performance optimization of affine loop nests on GPGPUs: 1) approach to program transformation for efficient data access from GPU global memory, using a polyhedral compiler model of data dependence abstraction and program transformation; 2) determination of optimal padding factors for conflict-minimal data access from GPU shared memory; and 3) model-driven empirical search to determine optimal parameters for unrolling and tiling. Experimental results on a number of kernels demonstrate the effectiveness of the compiler optimization approaches developed.
Arctic Tundra Vegetation Functional Types Based on Photosynthetic Physiology and Optical Properties

Non-vascular plants (lichens and mosses) are significant components of tundra landscapes and may respond to climate change differently from vascular plants affecting ecosystem carbon balance. Remote sensing provides critical tools for monitoring plant cover types, as optical signals provide a way to scale from plot measurements to regional estimates of biophysical properties, for which spatial-temporal patterns may be analyzed. Gas exchange measurements were collected for pure patches of key vegetation functional types (lichens, mosses, and vascular plants) in sedge tundra at Barrow AK. These functional types were found to have three significantly different values of light use efficiency (LUE) with values of 0.013±0.001, 0.0018±0.0002, and 0.0012±0.0001 C mol-1 absorbed quanta for vascular plants, mosses and lichens, respectively. Discriminant analysis of the spectra reflectance of these patches identified five spectral bands that separated each of these vegetation functional types as well as nongreen material (bare soil, standing water, and dead leaves). These results were tested along a 100 m transect where midsummer spectral reflectance and vegetation coverage were measured at one meter intervals.
Understanding Model Averaging in Federated Learning on Heterogeneous Data

Model averaging, a widely adopted technique in federated learning (FL), aggregates multiple client models trained on heterogeneous data to obtain a well-performed global model. However, the rationale behind its success is not well understood. To shed light on this issue, we investigate the geometric properties of model averaging by visualizing the loss/error landscape. The geometrical visualization shows that the client models surround the global model within a common basin, and the global model may deviate from the bottom of the basin even though it performs better than the client models. To further understand this phenomenon, we decompose the expected prediction error of the global model into ﬁve factors related to client models. Speciﬁcally, we ﬁnd that the global-model error after early training mainly comes from i) the client-model error on non-overlapping data between client datasets and the global dataset and ii) the maximal distance between the global and client models. Inspired by these ﬁndings, we propose adopting iterative moving averaging (IMA) on global models to reduce the prediction error and limiting client exploration to control the maximal distance at the late training. Our experiments demonstrate that IMA signiﬁcantly improves the accuracy and training speed of existing FL methods on benchmark datasets with various data heterogeneity.
Lambda-Field: A Continuous Counterpart Of The Bayesian Occupancy Grid For Risk Assessment And Safe Navigation

In the context of autonomous robots, one of the most important tasks is to prevent potential damages on the robot during navigation. For this purpose, it is often assumed to deal with known probabilistic obstacles, then to compute the probability of collision to each obstacle. However, in complex scenarios or unstructured environments, it might be difficult to detect such obstacles. In this case, a metric map is used where each position stores the information of occupancy. The most common type of metric map is the bayesian occupancy map. However, this type of map is not well fitted to compute risk assessment for continuous paths due to its discrete nature. Hence, we introduce a novel type of map called Lambda-Field, specially designed for risk assessment. We first propose a way to compute such a map and the expectancy of a generic risk over a path. Then, we demonstrate the utility of our generic formulation with a use case defining the risk as the expected force of collision over a path. Using this risk definition and the Lambda-Field, we show that our framework is capable of doing classical path planning while having a physical-based metric. Furthermore, the Lambda-Field gives a natural way to deal with unstructured environments like tall grass. Where standard environment representations would generate trajectories going around such obstacles, our framework allows the robot to go through the grass while being aware of the risk taken.
Secret Ballot Elections with Unconditional Integrity

This paper describes in detail a voting scheme which allows voters to be sure that whatever they see in the booth will be included correctly in the outcome. It presents a rigorous and understandable model of requirements for election systems, states formally the properties of the system, and proves them. As a step towards understanding the most general voting system that addresses write-in ballots, it also presents a simpler system for pre-determined candidate lists. © 2007 University of Newcastle upon Tyne. Printed and published by the University of Newcastle upon Tyne, Computing Science, Claremont Tower, Claremont Road, Newcastle upon Tyne, NE1 7RU, England. Bibliographical details CHAUM, D. , VAN DER GRAAF, J., RYAN, P. Y. A. , VORA, P. Secret Ballot Elections with Unconditional Integrity [By] D. Chaum, J. van der Graaf, P. Y. A. Ryan and P. Vora. Newcastle upon Tyne: University of Newcastle upon Tyne: Computing Science, 2007. (University of Newcastle upon Tyne, Computing Science, Technical Report Series, No. CS-TR-1058)
Memory-E cient Algorithms for theVeri cationof Temporal PropertiesC

This paper addresses the problem of designing memory-eecient algorithms for the veriication of temporal properties of nite-state programs. Both the programs and their desired temporal properties are modeled as automata on innnite words (B uchi automata). Veri-cation is then reduced to checking the emptiness of the automaton resulting from the product of the program and the property. This problem is usually solved by computing the strongly connected components of the graph representing the product automaton. Here, we present algorithms which solve the emptiness problem without explicitly constructing the strongly connected components of the product graph. By allowing the algorithms to err with some probability, we 1 can implement them with a randomly accessed memory of size O(n) bits, where n is the number of states of the graph, instead of O(n logn) bits that the presently known algorithms require.
A knowledge server for reasoning about temporal constraints between classes and instances of events

Reasoning with temporal constraints is a ubiquitous issue in many computer science tasks, for which many dedicated approaches have been and are being built. In particular, in many areas, including planning, workflow, guidelines, and protocol management, one needs to represent and reason with temporal constraints between classes of events (e.g., between the types of actions needed to achieve a goal) and temporal constraints between instances of events (e.g., between the specific actions being executed). The temporal constraints between the classes of events must be inherited by the instances, and the consistency of both types of constraints must be checked. In this article, we design a general-purpose domain-independent knowledge server dealing with these issues. In particular, we propose a formalism to represent temporal constraints, and we point out two orthogonal parameters that affect the definition of reasoning algorithms operating on them. We then show four algorithms to deal with inheritance and to perform temporal consistency checking (depending on the parameters) and we study their properties. Finally, we report the results we obtained by applying our system to the treatment of temporal constraints in clinical guidelines. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 919–947, 2004.