Miracle-dz/UniFAR-FTUs huggingface dataset:
DOT1L regulates chromatin reorganization and gene expression during sperm differentiation

Spermatozoa have a unique genome organization: their chromatin is almost completely devoid of histones and is formed instead of protamines which confer a high level of compaction and preserve paternal genome integrity until fertilization. Histone-to-protamine transition takes place in spermatids and is indispensable for the production of functional sperm. Here we show that the H3K79-methyltransferase DOT1L controls spermatid chromatin remodelling and subsequent reorganization and compaction of spermatozoon genome. Using a mouse model in which Dot1l is knocked-out (KO) in postnatal male germ cells, we found that Dot1l-KO sperm chromatin is less compact and has an abnormal content, characterized by the presence of transition proteins, immature protamine 2 forms and a higher level of histones. Proteomics and transcriptomics analyses performed on spermatids reveal that Dot1l-KO modifies the chromatin prior to histone removal, and leads to the deregulation of genes involved in flagellum formation and apoptosis during spermatid differentiation. As a consequence of these chromatin and gene expression defects, Dot1l-KO spermatozoa have less compact heads and are less motile which results in impaired fertility.
Phonon spectrum and specific heat of silicon nanowires

Based on lattice dynamics theory and molecular dynamics simulations, we have investigated the geometrical structures, phonon dispersion relations, and specific heat of silicon nanowires with Stillinger-Weber potential. It was shown that the original Stillinger-Weber potential can reproduce the well-established four acoustical branches. With the calculated spectra, we calculated specific heats of silicon nanowires. It is found that the specific heats of thin nanowires are much higher than those of bulk silicon. According to the partial density of states of surface atoms, the enhancement of specific heats of silicon nanowires can be attributed to the surface effect and phonon confinement effect.
A Step Towards Generalisability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening

Over the last few years, many machine learning-based scoring functions for predicting the binding of small molecules to proteins have been developed. Their objective is to approximate the distribution which takes two molecules as input and outputs the energy of their interaction. Only a scoring function that accounts for the interatomic interactions involved in binding can accurately predict binding affinity on unseen molecules. However, many scoring functions make predictions based on dataset biases rather than an understanding of the physics of binding. These scoring functions perform well when tested on similar targets to those in the training set, but fail to generalise to dissimilar targets. To test what a machine learning-based scoring function has learnt, input attribution—a technique for learning which features are important to a model when making a prediction on a particular data point—can be applied. If a model successfully learns something beyond dataset biases, attribution should give insight into the important binding interactions that are taking place. We built a machine learning-based scoring function that aimed to avoid the influence of bias via thorough train and test dataset filtering, and show that it achieves comparable performance on the CASF-2016 benchmark to other leading methods. We then use the CASF-2016 test set to perform attribution, and find that the bonds identified as important by PointVS, unlike those extracted from other scoring functions, have a high correlation with those found by a distance-based interaction profiler. We then show that attribution can be used to extract important binding pharmacophores from a given protein target when supplied with a number of bound structures. We use this information to perform fragment elaboration, and see improvements in docking scores compared to using structural information from a traditional, data-based approach. This not only provides definitive proof that the scoring function has learnt to identify some important binding interactions, but also constitutes the first deep learning-based method for extracting structural information from a target for molecule design.
The mRNA decapping machinery targets LBD3/ASL9 to mediate apical hook and lateral root development

This study identifies LBD3/ASL9 as targets of mRNA decay machinery, and ASL9 contributes to apical hook and lateral root development, possibly through interfering with cytokinin/auxin responses. Multicellular organisms perceive and transduce multiple cues to optimize development. Key transcription factors drive developmental changes, but RNA processing also contributes to tissue development. Here, we report that multiple decapping deficient mutants share developmental defects in apical hook, primary and lateral root growth. More specifically, LATERAL ORGAN BOUNDARIES DOMAIN 3 (LBD3)/ASYMMETRIC LEAVES 2-LIKE 9 (ASL9) transcripts accumulate in decapping deficient plants and can be found in complexes with decapping components. Accumulation of ASL9 inhibits apical hook and lateral root formation. Interestingly, exogenous auxin application restores lateral roots formation in both ASL9 over-expressors and mRNA decay–deficient mutants. Likewise, mutations in the cytokinin transcription factors type-B ARABIDOPSIS RESPONSE REGULATORS (B-ARRs) ARR10 and ARR12 restore the developmental defects caused by over-accumulation of capped ASL9 transcript upon ASL9 overexpression. Most importantly, loss-of-function of asl9 partially restores apical hook and lateral root formation in both dcp5-1 and pat triple decapping deficient mutants. Thus, the mRNA decay machinery directly targets ASL9 transcripts for decay, possibly to interfere with cytokinin/auxin responses, during development.
Mechanical Properties of DNA Double-Crossover Motif

The double-crossover (DX) motif is a key building block of DNA nanostructures. It connects two double helices by two closely spaced Holliday junctions. Despite its prominent importance, the structure and elasticity of the DX motif is not fully understood. Here we employ extensive all-atom molecular dynamics (MD) simulations of an antiparallel DNA DX motif with two full turns between crossovers to infer its global structure and deformability. We quantitatively reproduce the experimentally known two-fold increase of bending stiffness upon incorporation of a DNA duplex into the DX motif, and find out that its stretching and twisting stiffness are only slightly influenced. To further describe the motif, we define four effective rigid bodies, each involving several base pairs flanking the Holliday junctions from the outside, and consider internal coordinates capturing relative displacement and orientation of the bodies. Time series of the coordinates from MD then yield the global structure of the DX motif and its stiffness in the harmonic approximation.
What mycologists should talk about when they are talking about the International Code of Nomenclature for algae, fungi, and plants

Fungal metabarcoding of substrates such as soil, wood, and water are uncovering an unprecedented number of fungal species that do not seem to produce tangible morphological structures and that defy our best attempts at cultivation, thus falling outside of the ambit of the International Code of Nomenclature for algae, fungi, and plants. The present study uses the new, ninth release of the species hypotheses of the UNITE database to show that species discovery through environmental sequencing vastly outpaces traditional, Sanger sequencing-based efforts in a strongly increasing trend over the last five years. Our findings challenge the present stance of the mycological community – that “the code” works fine and that these complications will somehow sort themselves out given enough time and a following wind – and suggest that we should be discussing not whether to allow DNA-based descriptions (typifications) of species and by extension higher ranks of fungi, but what the precise requirements for such DNA-based typifications should be. We submit a tentative list of such criteria for further discussion. However, the present authors fear that no waves of change will be lapping the shores of mycology for the foreseeable future, leaving the overwhelming majority of extant fungi without formal names and thus scientific and environmental agency. It is not clear to us who benefits from that, but neither fungi nor mycology are likely to be on the winning side.
On the quest for novelty in ecology

The volume of scientific publications is ever-increasing, making it difficult for scholars to publish papers that can capture the attention of readers. An obvious way to attract readership is by making a truly significant discovery; yet another way may involve tweaking the language to overemphasize the novelty of results. Using a dataset of 52,236 paper abstracts published between 1997 and 2017 in 17 ecological journals, we inspected whether the relative frequency of the use of novelty (e.g. ‘groundbreaking’, ‘new’) and confirmatory (e.g. ‘replicated’, ‘reproducibility’) terms has increased over time. Further, we tested whether relationships exist between the use of these terms and either Impact Factor of the journal a paper had been published in or number of citations a paper had received. The frequency of novelty terms almost doubled between 1997 and 2017, and was positively related to the journal Impact Factor and the number of citations. Conversely, no such patterns were found for confirmatory terms. We argue that, while increasing research opportunities are possibly triggering advances in ecology, the writing style and publishing habits should better reflect the inherent confirmatory nature of ecological research. The possible causes and consequences that such language-use matter may have for the scientific and broader community remain unknown, and we call for opening a discussion among researchers.
EiDA: A lossless approach for the dynamic analysis of connectivity patterns in signals; application to resting state fMRI of a model of ageing

Dynamic Functional Connectivity (dFC) is the study of the dynamical patterns emerging from brain function. We introduce EiDA (Eigenvector Dynamic Analysis), a method that losslessly reduces the dimension of the instantaneous connectivity patterns of a time series to characterise dynamic Functional Connectivity (dFC). We apply EiDA to investigate the signatures of ageing on brain network dynamics in a longitudinal dataset of resting-state fMRI in ageing rats. Previous dFC approaches have relied on the concept of the instantaneous phase of signals, computing the instantaneous phase-locking matrix (iPL) and its eigenvector decomposition. In this work, we fully characterise the eigenstructure of the iPL analytically, which provides a 1000 fold speed up in dFC computations. The analytical characterization of the iPL matrix allows us to introduce two methods for its dynamic analysis. 1) Discrete EiDA identifies a discrete set of phase locking modes using k-means clustering on the decomposed iPL matrices. 2) Continuous EiDA provides a 2-dimensional “position” and “speed” embedding of the matrix; here, dFC is conceived as a continuous exploration of this 2-D space rather than assuming the existence of discrete brain states. We apply EiDA to a cohort of 48 rats that underwent functional magnetic resonance imaging (fMRI) at four stages during the course of their lifetime. Using Continuous and Discrete EiDA we found that brain phase-locking patterns become less intense and less structured with ageing. Using information theory and metastability measures derived from the properties of the iPL matrix, we see that ageing reduces the available functional repertoire postulated to be responsible for flexible cognitive functions and overt behaviours, and reduces the area explored in the embedding space.
Identification of efflux substrates using a riboswitch-based reporter in Pseudomonas aeruginosa

Pseudomonas aeruginosa is intrinsically resistant to many classes of antibiotics, reflecting the restrictive nature of its outer membrane and the action of its numerous efflux systems. However, the dynamics of compound uptake, retention and efflux in this bacterium remain incompletely understood. Here, we exploited the sensor capabilities of a Z-nucleotide sensing riboswitch to create an experimental system able to identify physicochemical and structural properties of compounds that permeate the bacterial cell, avoid efflux, and perturb the folate cycle or de novo purine synthesis. In a first step, a collection of structurally diverse compounds enriched in antifolate drugs was screened for ZTP riboswitch reporter activity in efflux-deficient P. aeruginosa, allowing us to identify compounds that entered the cell and disrupted the folate pathway. These initial hits were then rescreened using isogenic efflux-proficient bacteria, allowing us to separate efflux substrates from efflux avoiders. We confirmed this categorization by measuring intracellular levels of select compounds in the efflux-deficient and - proficient strain using high resolution LC-MS. This simple yet powerful method, optimized for high throughput screening, enables the discovery of numerous permeable compounds that avoid efflux and paves the way for further refinement of the physicochemical and structural rules governing efflux in this multi-drug resistant Gram-negative pathogen. Importance Treatment of Pseudomonas aeruginosa infections has become increasingly challenging. The development of novel antibiotics against this multi-drug resistant bacterium is a priority, but many drug candidates never achieve effective concentrations in the bacterial cell due due to its highly restrictive outer membrane and the action of multiple efflux pumps. Here, we develop a robust and simple reporter system in P. aeruginosa to screen chemical libraries and identify compounds that either enter the cell and remain inside, or enter the cell and are exported by efflux systems. This approach enables developing rules of compound uptake and retention in P. aeruginosa that will lead to more rational design of novel antibiotics.
Guiding protein design choices by per-residue energy breakdown analysis with an interactive web application

Recent developments in machine learning have greatly facilitated the design of proteins with improved properties. However, accurately assessing the contributions of an individual or multiple amino acid mutations to overall protein stability to select the most promising mutants remains a challenge. Knowing the specific types of amino acid interactions that improve energetic stability is crucial for finding favorable combinations of mutations and deciding which mutants to test experimentally. In this work, we present an interactive workflow for assessing the energetic contributions of single and multi-mutant designs of proteins. The energy breakdown guided protein design (ENDURE) workflow includes several key algorithms, including per-residue energy analysis and the sum of interaction energies calculations, which are performed using the Rosetta energy function, as well as a residue depth analysis, which enables tracking the energetic contributions of mutations occurring in different spatial layers of the protein structure. ENDURE is available as a web application that integrates easy-to-read summary reports and interactive visualizations of the automated energy calculations and helps users selecting protein mutants for further experimental characterization. We demonstrate the effectiveness of the tool in identifying the mutations in a designed polyethylene terephthalate (PET)-degrading enzyme that add up to an improved thermodynamic stability. We expect that ENDURE can be a valuable resource for researchers and practitioners working in the field of protein design and optimization. ENDURE is freely available for academic use at: http://endure.kuenzelab. org.
MadFS: Per-File Virtualization for Userspace Persistent Memory Filesystems

Persistent memory (PM) can be accessed directly from userspace without kernel involvement, but most PM filesystems still perform metadata operations in the kernel for security and rely on the kernel for cross-process synchronization. We present per-file virtualization, where a virtualization layer implements a complete set of file functionalities, including metadata management, crash consistency, and concurrency control, in userspace. We observe that not all file metadata need to be maintained by the kernel and propose embedding insensitive metadata into the file for userspace management. For crash consistency, copy-on-write (CoW) benefits from the embedding of the block mapping since the mapping can be efficiently updated without kernel involvement. For cross-process synchronization, we introduce lockfree optimistic concurrency control (OCC) at user level, which tolerates process crashes and provides better scalability. Based on per-file virtualization, we implement MadFS, a library PM filesystem that maintains the embedded metadata as a compact log. Experimental results show that on concurrent workloads, MadFS achieves up to 3.6× the throughput of ext4-DAX. For real-world applications, MadFS provides up to 48% speedup for YCSB on LevelDB and 85% for TPC-C on SQLite compared to NOVA.
A Cloud-based Continual Learning System for Road Sign Classification in Autonomous Driving

Artificial intelligence and deep learning have demonstrated highly promising results in challenging problems, such as autonomous or assisted driving. One challenge in the integration of these solutions in real-life applications, is that they often operate in a resource-constrained edge environment. Another important challenge is the ability of the AI system to adapt and expand its abilities in a constantly changing environment. Constant changes could potentially cause significant deterioration of a model’s effectiveness, a phenomenon called Catastrophic Forgetting . In this paper, we propose a Continual Learning framework for efficient and continuous update of a road sign classification system for assisted or autonomous driving. Our proposition considers the limitations of edge computing and utilizes a cloud infrastructure. Test results show that the our proposition is capable of expanding an edge models knowledge in a stable manner.
Ride-hailing applications in Southeast Asia: A literature review

Abstract This paper provides a review of the literature on ride-hailing applications (RHA) in Southeast Asia (SEA) to enhance the understanding of RHA’s impacts on the urban transportation sector and to inform related planning and regulatory efforts in the context of developing countries. The conceptual framework is based on the stakeholder approach, which identifies three groups of stakeholders: the demand-side, the supply-side, and the public-sector stakeholders. A search on scholarly databases yielded 49 related articles. The results of the review illustrate certain similarities between RHA services in SEA with those reported from developed countries, such as the socio-economic profile of the users. However, several observations unique to the SEA context were also revealed, including (1) the dominance of motorcycles; (2) commuting as the main RHA trip purpose; (3) a higher frequency of RHA use; and (4) a significant proportion of full-time RHA drivers. The review also highlighted research gaps in the literature of RHA in SEA, particularly on how RHA can influence travelers’ behavior, its effects on the incumbent transport operators, and its environmental impacts.
High-k gate stack on germanium substrate with fluorine incorporation

In this letter, a postgate CF4-plasma treatment is proposed and demonstrated on germanium (Ge) metal-oxide-semiconductor capacitors and the effects of fluorine (F) incorporation have been studied on both high-k∕Ge gate stacks without any surface passivation and with Si surface passivation. Our results show that F is effectively introduced into the gate stack by CF4 treatment and segregates near high-k∕Ge interface. Electrical characteristics such as frequency dispersion, interface state density (Dit), and gate leakage are improved after F incorporation. Interface quality of high-k∕Ge gate stack is further improved by combining Si surface passivation and postgate CF4 treatment, with its Dit as low as 4.85×1011cm−2eV−1.
Flexural behavior of high-performance and non-high-performance textile reinforced concrete composites

Abstract The flexural behaviour of textile reinforced concrete (TRC) samples reinforced with two types of textile, (1) polypropylene (PP) roving with a low elastic modulus and (2) aramid roving with a high elastic modulus, is studied and compared. The TRC samples are reinforced using up to eight layers of plain weaved fabrics with the mesh size of 5 mm and 10 mm. Flexural behaviour and capacity of composite specimens are investigated using a four-point bending test. The microstructure of cement matrix and fibres is also studied using SEM images. It was found that the flexural strength of TRC samples strengthened with PP and Aramid textiles increased by increasing the number of fabric layers. Reducing the fabric mesh size increased the bending strength of Aramid TRC samples because of the chemical bond between Aramid fibres and concrete. On the contrary, PP fabrics with larger mesh sizes showed higher bending moment strength as the connection between PP fabrics and concrete is mechanical. A large number of PP fabrics in TRC specimens caused delamination; therefore, roving reinforced concrete (RRC) instead of TRC was used to increase the reinforcement ratio in specimens. The test results of TRC and RRC samples showed that the flexural behaviour of samples reinforced with PP longitudinal roving (reinforcement ratio of 1.60% PP roving) was similar or even better than samples reinforced with high-modulus Aramid fabrics (reinforcement ratio of 0.92% Aramid fabric) in terms of strength and stiffness.
This paper is included in the Proceedings of the 21st USENIX Conference on File and Storage Technologies.

Byte-addressable non-volatile memory (NVM) allows programs to directly access storage using memory interface without going through the expensive conventional storage stack. However, direct access to NVM makes the NVM data vulnerable to software bugs and hardware errors. This issue is critical because, unlike DRAM, corrupted data can persist forever, even after the system restart. Albeit the plethora of research on NVM programs and systems, there is little focus on protecting NVM data from software bugs and hardware errors. In this paper, we propose T ENET , a new NVM programming framework, which guarantees memory safety and fault tolerance to protect NVM data against software bugs and hardware errors. T ENET provides the popular persistent transactional memory (PTM) programming model. T ENET leverages the concurrency guarantees ( i . e ., ACID properties) of PTM to provide performant and cost-efficient memory safety and fault tolerance. Our evaluations show that T ENET offers an enhanced protection scope at a modest performance overhead and storage cost as compared to other PTMs with partial or no memory safety and fault tolerance support.