Miracle-dz/UniFAR-FTUs huggingface dataset:
Single-cell strand sequencing of a macaque genome reveals multiple nested inversions and breakpoint reuse during primate evolution

Rhesus macaque is an Old World monkey that shared a common ancestor with human ∼25 Myr ago and is an important animal model for human disease studies. A deep understanding of its genetics is therefore required for both biomedical and evolutionary studies. Among structural variants, inversions represent a driving force in speciation and play an important role in disease predisposition. Here we generated a genome-wide map of inversions between human and macaque, combining single-cell strand sequencing with cytogenetics. We identified 375 total inversions between 859 bp and 92 Mbp, increasing by eightfold the number of previously reported inversions. Among these, 19 inversions flanked by segmental duplications overlap with recurrent copy number variants associated with neurocognitive disorders. Evolutionary analyses show that in 17 out of 19 cases, the Hominidae orientation of these disease-associated regions is always derived. This suggests that duplicated sequences likely played a fundamental role in generating inversions in humans and great apes, creating architectures that nowadays predispose these regions to disease-associated genetic instability. Finally, we identified 861 genes mapping at 156 inversions breakpoints, with some showing evidence of differential expression in human and macaque cell lines, thus highlighting candidates that might have contributed to the evolution of species-specific features. This study depicts the most accurate fine-scale map of inversions between human and macaque using a two-pronged integrative approach, such as single-cell strand sequencing and cytogenetics, and represents a valuable resource toward understanding of the biology and evolution of primate species.
Addressing H-Material Interaction in Fast Diffusion Materials—A Feasibility Study on a Complex Phase Steel

Hydrogen embrittlement (HE) is one of the main limitations in the use of advanced high-strength steels in the automotive industry. To have a better understanding of the interaction between hydrogen (H) and a complex phase steel, an in-situ method with plasma charging was applied in order to provide continuous H supply during mechanical testing in order to avoid H outgassing. For such fast-H diffusion materials, only direct observation during in-situ charging allows for addressing H effects on materials. Different plasma charging conditions were analysed, yet there was not a pronounced effect on the mechanical properties. The H concentration was calculated while using a simple analytical model as well as a simulation approach, resulting in consistent low H values, below the critical concentration to produce embrittlement. However, the dimple size decreased in the presence of H and, with increasing charging time, the crack propagation rate increased. The rate dependence of flow properties of the material was also investigated, proving that the material has no strain rate sensitivity, which confirmed that the crack propagation rate increased due to H effects. Even though the H concentration was low in the experiments that are presented here, different technological alternatives can be implemented in order to increase the maximum solute concentration.
A Quartz Crystal Microbalance, Which Tracks Four Overtones in Parallel with a Time Resolution of 10 Milliseconds: Application to Inkjet Printing

A quartz crystal microbalance (QCM) is described, which simultaneously determines resonance frequency and bandwidth on four different overtones. The time resolution is 10 milliseconds. This fast, multi-overtone QCM is based on multi-frequency lockin amplification. Synchronous interrogation of overtones is needed, when the sample changes quickly and when information on the sample is to be extracted from the comparison between overtones. The application example is thermal inkjet-printing. At impact, the resonance frequencies change over a time shorter than 10 milliseconds. There is a further increase in the contact area, evidenced by an increasing common prefactor to the shifts in frequency, Δf, and half-bandwidth, ΔΓ. The ratio ΔΓ/(−Δf), which quantifies the energy dissipated per time and unit area, decreases with time. Often, there is a fast initial decrease, lasting for about 100 milliseconds, followed by a slower decrease, persisting over the entire drying time (a few seconds). Fitting the overtone dependence of Δf(n) and ΔΓ(n) with power laws, one finds power-law exponents of about 1/2, characteristic of semi-infinite Newtonian liquids. The power-law exponents corresponding to Δf(n) slightly increase with time. The decrease of ΔΓ/(−Δf) and the increase of the exponents are explained by evaporation and formation of a solid film at the resonator surface.
Chipped or Whole? Listening to Survivors’ Experiences With Disclosure Following Sexual Violence

Sexual violence is a prevalent crime but vastly underreported and with serious long-term health consequences for survivors. Disclosure of sexual violence represents a social experience that may offer support towards healing or further traumatization depending on the response received. Although current research suggests that process of disclosure itself is important, as are social responses, there is a dearth of research examining the perceived impact of initial responses to disclosure on healing and relationships, particularly over time. The current study used data from nine focus groups with 45 survivors to explore the impact of initial disclosure reactions on recovery, from the survivors’ perspectives. Constant comparative analysis identified several themes, including subtypes of positive and negative responses to disclosure and long-term impacts on healing and relationships. Survivors disclosed to informal and formal support persons and although many identified responses as positive or negative, some also experienced mixed responses. Survivors identified perceived long-term impacts on healing, interpersonal relationships, and social justice. Our findings suggest disclosures are a critical point for potential intervention after sexual violence. It is through the disclosure process that survivors can be supported and empowered to connect with others and move further along in their journey towards healing and recovery. Public awareness and promotion of positive responses could be designed to reach children and youth, so that the next generation is equipped with the tools to support each other in difficult times, particularly in the aftermath of sexual violence.
Amyloid formation of fish β-parvalbumin involves primary nucleation triggered by disulfide-bridged protein dimers

Significance Amyloid fibrils are generally related to neurodegenerative diseases, but they can also be part of normal protein function. Amyloid formation involves numerous steps and intermediate species. In this study, we investigated a fish protein, beta-parvalbumin, which readily forms amyloid on ligand removal. Using biophysical experiments, we provide evidence that the underlying mechanism of amyloid formation includes primary nucleation and elongation processes; we also reveal a key role for a disulfide-bridged dimer in the nucleation step. Little is known about intermolecular disulfides in amyloid formation, but covalent dimers and dimer-induced aggregation may be of clinical relevance, because oxidative stress, which can trigger covalent bond formation, is often a hallmark of human neurodegenerative diseases. Amyloid formation involves the conversion of soluble protein species to an aggregated state. Amyloid fibrils of β-parvalbumin, a protein abundant in fish, act as an allergen but also inhibit the in vitro assembly of the Parkinson protein α-synuclein. However, the intrinsic aggregation mechanism of β-parvalbumin has not yet been elucidated. We performed biophysical experiments in combination with mathematical modeling of aggregation kinetics and discovered that the aggregation of β-parvalbumin is initiated by the formation of dimers stabilized by disulfide bonds and then proceeds via primary nucleation and fibril elongation processes. Dimer formation is accelerated by H2O2 and hindered by reducing agents, resulting in faster and slower aggregation rates, respectively. Purified β-parvalbumin dimers readily assemble into amyloid fibrils with similar morphology as those formed when starting from monomer solutions. Furthermore, addition of preformed dimers accelerates the aggregation reaction of monomers. Aggregation of purified β-parvalbumin dimers follows the same kinetic mechanism as that of monomers, implying that the rate-limiting primary nucleus is larger than a dimer and/or involves structural conversion. Our findings demonstrate a folded protein system in which spontaneously formed intermolecular disulfide bonds initiate amyloid fibril formation by recruitment of monomers. This dimer-induced aggregation mechanism may be of relevance for human amyloid diseases in which oxidative stress is often an associated hallmark.
In-vitro Antitumor Activity and Metabolic Finger Printing of the Actinomycetes Isolated from Various Ecological Niches in Pakistan

Actinomycetes are Gram positive bacteria with high G+C content in their DNA and are capable of producing variety of secondary metabolites. Many of these metabolites possess different biological activities and have the potential to be developed as therapeutic agents. The aim of the present study was to screen actinomycetes of various ecological niches in Pakistan for cytotoxic and antitumor compounds. Several water and soil samples were collected from different areas in Pakistan and more than 500 actinomycete isolates were recovered by selective isolation techniques. Among them 120 isolates were selected on different morphological basis for identification and cytotoxicity. These isolates were identified on the basis of their Microscopy (Scanning electron microscopy), morphological, biochemical and physiological behavior's along with genetic characterization. The isolates such as MKA 17, SSA 13 and KML 2 showed highest mortality rate with having 92%, 84% and 84% dead larvae (Anemia salina) after 24 hours respectively. Theses potent isolates were tested against proliferative cell lines by Methyl Thiazolyl Tetrazolium (MTT) bioassay method. Isolate KML, 2 and SSA 13 showed highest antitumor activity with having IC50 values of 12.17 mu g/ml and 16.4 mu g/ml against HeLa cell line respectively. Chemical screening profile by TLC and robust UPLC-MS showed distinctive chemical diversity in the extracts of selected isolates. Indeed, preparative screening of isolated actinomycetes strains delivered bioactive compounds including actinomycin D and resistomycin from SSA 13 and MKA 17 respectively. The genetic characterization by 16S rRNA gene sequencing exhibited maximum genetic similarity with different species of the well-known genus of actinomycetes named as Streptomyces.
Suspicious Local Event Detection in Social Media and Remote Sensing: Towards a Geosocial Dataset Construction

Remote sensing is a powerful technology for earth observation. However, the spatial, spectral, and temporal resolution of the imagery are imposing various limits. Lately, with the rise of the internet and smart mobile devices, social media with location-based information has been rapidly emerging. These circumstances led to the prevailing of new scenarios where fine-grained details of social bookmarking websites are enhanced with the wide coverage of satellites. Social media and satellites are both valuable sources of data. An event-driven data, designating either normal common events or unusual suspicious ones that may threaten human lives or damage the infrastructure. In this paper, we provide an insight into the present state of knowledge to better address the task of local suspicious event detection and linking social media with satellite imagery. Also, to track suspicious local events, we treated the detection problem as a retrospective problem by training different classifiers on the crisisLexT26 dataset. Furthermore, we introduced how to use the available geo-locations in the dataset to construct a geo-social dataset by linking it with remote sensing and retrieving satellite imagery before and after the event occurrence.
Detection of Hidden Transformer Tap Change Command Attacks in Transmission Networks

Transformer taps are used extensively to regulate bus voltages in transmission networks. Tap change commands relayed through the SCADA network are attractive targets for falsification by attackers in order to adversely affect the operation of the system. Such attacks can be hidden by selective measurement manipulations. In this paper, an algorithm is developed that detects the presence of a stealthy false tap change command. The development of the algorithm is based on the intuition that any attack involving injection of false data or commands can only influence the measurement and estimation of certain selected variables, not all of them. The algorithm is based on the ratios of injection or branch currents to the voltages of the terminal nodes of the tap changing transformers. This principle is proven analytically and validated using simulations, leading to the establishment of an index which distinguishes stealthy attacks from normal operation scenarios. This lead to the development of an algorithm which is simple to implement, computationally light and shown to be extremely reliable when tested across various cases on the IEEE 118-bus and 2383-bus Polish systems.
A Survey of Computation Offloading in Edge Computing

Computation offloading is a critical technology for emerging edge computing and Internet of things (IoT). It is views as a solution of the limited resources of IoT devices by offloading tasks to other devices or servers. It can brings many benefits, such as prolonging battery life, reducing the latency and improving application performance. In practice, the effect of computation offloading is affected by many factors, which makes many offloading unable to achieve their expected objectives. In this paper, we present a comprehensive survey of computation offloading in edge computing including offloading scenarios, influence factors and offloading strategies. Particularly, we discuss key issues through the offloading process, such as whether, where, what to offload.
An effective approach for the diagnosis of melanoma using the sparse auto-encoder for features detection and the SVM for classification

Malignant melanoma is considered one of the terrible disorders causing death. The goal of the modern dermatology is the early screening of skin cancer, aiming at reducing the mortality rate with less extensive treatment. In this context, this work focuses on the problem of an automatic melanoma diagnosis. The proposed approach uses unsupervised robustness of deep learning to extract significant characteristics from pixels of the images. A preprocessing step is used to remove unwanted artifacts and to improve the contrast of the images. Then, features are extracted by a deep Sparse Auto-encoder. Finally, the classifier Support Vector Machine (SVM) is used to distinguish respectively between three populations which are Melanoma, suspicious cases, and non-melanoma. For evaluation, we test the proposed approach using images from the PH2 dataset. The results show remarkable performance in terms of specificity, sensitivity, and accuracy.
Insulator Defect Recognition Based on Faster R-CNN

Insulators are important parts to ensure the normal operation of transmission lines. The traditional method is to judge the defect of the insulators through human eyes, which is not only low in efficiency, but also strong and dangerous in work. In this paper, the most representative Resnet-50 and Faster R-CNN frameworks in target classification algorithms are used for training to detect and recognize insulator targets, and introduce target network model detection performance indicators. The results show that this method can judge the defect of insulators well, and has strong robustness and practicability. Reduce manpower and improve accuracy and timeliness.
Sparse Aperture Three-dimensional Reconstruction of Precession Target Based on Compressed Sensing

As a kind of high speed rotation object, precession target is faced with migration through resolution cell (MTRC) in long synthetic aperture while using translational inverse synthetic aperture radar (ISAR) imaging algorithm. Compressed sensing (CS), by which we can exact recovery sparse signal from very limited samples, suggests that sparse aperture imaging of precession target maybe achievable. A cyclic shift algorithm based on CS is proposed in this paper to exploit the sparse apertures data for high-resolution ISAR imaging. The sparse signal recovery and imaging of precession target is achieved coupled with FOCUSS (focal undetermined system solver) algorithm. A conventional ISAR imaging is a two-dimensional (2-D) range-Doppler projection of a target and does not provide three-dimensional (3-D) information which is more reliable. For missile shaped like a flat-bottom cone, multistatic ISAR geometry model is built, and a 3-D reconstruction method, which is featured with stable structure characteristics, is proposed based on multistatic ISAR images. Simulation and real data results verify the validity and superiority of the proposed method.
DNN-Based Decoder for Four-Dimensional Modulation Superposition NOMA

As a critical technology for the fifth-generation (5G) mobile communication system, non-orthogonal multiple access (NOMA) has drawn substantial attention due to its high spectrum efficiency with successive interference cancellation (SIC). However, SIC requires relatively high computation complexity since it needs to decode the interferer’s information first. In this paper, we propose a DNN-based decoder for four-dimensional modulation superposition NOMA. Spherical code is utilized as the four-dimensional modulation method to increase the Euclidean distance between constellations. We design the DNN-based decoder and analyze the effect of different training set on the detection performance. The performance of the DNN-based decoder is compared with the traditional maximum likelihood (ML) decoder. The simulation results show that, the DNN-based decoder can work well with a low complexity.
A Method for “Last mile” Distribution Demand for Drones

To solve the problem of drone demand for “last mile” distribution, many factors such as drone performance, airspace environment and transportation tasks are considered. The objective function is to minimize transportation cost with a UAV demand model. Dynamic allocation algorithm can calculate the optimal demand on the drone. The actual delivery data and low-altitude flight conditions in a certain area are analyzed as examples. The results show that the optimal load of drones is 20kg under the initial parameters, and the optimal loads of drones are 20kg and 25kg when use different drones to delivery together. The method proposed in this paper can not only calculate the demand on unmanned aerial vehicles in this area within milliseconds, but also provide flexible demand schemes when styles of drones, the airspace open-door policies and the transportation demand vary.
Investigation of electrochemical behaviour of polyaniline on carbon screen-printed electrodes for the detection of nitrite

The existence of nitrite in food, water and environment can cause toxic effects on humans and there is a need for detection. Several approaches such as optical, chromatography and electrochemical techniques have been followed to detect nitrite. Electrochemical sensors are attractive because of their ease of preparation, low cost, fast response time and on/field measurement ability. We introduce the use of polyaniline polymer and carbon screen-printed electrode for detection of nitrite. Polyaniline is a conducting polymer with high environmental stability and conducting properties. The working electrode was electrochemically polymerized in different acid solutions. Here we show the effect of the different acids on the electro polymerization of aniline towards the electrochemical sensing properties for nitrite. It was investigated that polymerization in sulphuric acid was found to achieve the best sensitivity towards nitrite detection. The effect of number of scans and scan rate of electropolymerization had a significant effect on the detection of nitrite.
Recent Developments in Generative Adversarial Networks: A Review (Workshop Paper)

In recent times, Generative Adversarial Networks (GANs) have created a lot of buzz in the research community. GANs are formulated on the zero-sum game theory, where two neural nets compete against each other. The resultant deep model is capable of generating data similar to any data distribution provided. It utilizes the adversarial learning approach and is far more capable in learning features than the traditional machine learning models. This review focusses on the origin and evolution of GANs. Firstly, the traditional GAN is explored in terms of its structure and loss functions. Then come the common challenges of training GANs. Thirdly, the review dives into numerous GAN variants and explains their improvements. The review then lists the wide variety of applications and ends with the conclusion.
Efficient Compression Algorithm for Multimedia Data

In this work, we consider the problem of Cosine Similarity preserving dimensionality reduction (compression) for the sparse binary dataset. [18] suggested a compression algorithm for high dimensional, sparse, binary data for preserving Inner product and Hamming distance. In this work, we show that their proposed algorithm also works well for Cosine Similarity. We present a theoretical analysis of the dimension reduction bound and complement it with rigorous experimentation on real-world datasets. We compare our results with the state-of-the-art for the considered problem – SimHash [8], MinHash [21], Circulant Binary Embedding [25], and Densified one Permutation Hashing [20], and show that our result offers a significant saving in the compression time and the number of random bits required for the compression, and simultaneously provides comparable performance.
GALRU: A Group-Aware Buffer Management Scheme for Flash Storage Systems

Many flash storage systems divide input/output (I/O) requests that require large amounts of data into sub-requests to exploit their internal parallelism. In this case, an I/O request can be completed only after all sub-requests have been completed. Thus, non-critical sub-requests that are completed quickly do not affect I/O latency. To efficiently reduce I/O latency, we propose a buffer management scheme that allocates buffer space by considering the relationship between the processing time of the sub-request and I/O latency. The proposed scheme prevents non-critical sub-requests from wasting ready-to-use buffer space by avoiding the situation in which buffer spaces that are and are not ready to use are allocated to an I/O request. To allocate the same type of buffer space to an I/O request, the proposed scheme first groups sub-requests derived from the same I/O request and then applies a policy for allocating buffer space in units of sub-request groups. When the ready-to-use buffer space is insufficient to be allocated to the sub-request group being processed at a given time, the proposed scheme does not allocate it to the sub-request group but it instead sets it aside for future I/O requests. The results of the experiments to test the proposed scheme show that it can reduce I/O latency by up to 24% compared with prevalent buffer management schemes.