Miracle-dz/UniFAR-FTUs huggingface dataset:
Host specialization and molecular evidence support a distinct species of smut fungus, Anthracoidea hallerianae (Anthracoideaceae), on Carex halleriana (Cyperaceae)

Abstract: The species of Anthracoidea on Carex are host-specific smut fungi restricted to sedges belonging to the same or closely related sections. They are characterized by sori that form black, hard bodies around aborted nuts of their hosts. In Carex sect. Hallerianae, only one species, C. halleriana, is known as a host of Anthracoidea. The taxonomic status of this smut fungus was problematic due to a lack of molecular data. It has been reported under different names, mainly as “A. caricis” or “A. irregularis”. A comparative morphological study and molecular phylogenetic analysis, using LSU (large subunit) nuclear rDNA sequences, supported the recognition of a distinct species, A. hallerianae. The new species is described and illustrated based on material from Central Europe, the Iberian Peninsula, the Balkan Peninsula, the Mediterranean area and Transcaucasia. Citation: Denchev T. T. , Denchev C. M. , Koopman J., Begerow D. & Kemler M. 2021: Host specialization and molecular evidence support a distinct species of smut fungus, Anthracoidea hallerianae (Anthracoideaceae), on Carex halleriana (Cyperaceae). – Willdenowia 51: 57–67. Version of record first published online on 9 March 2021 ahead of inclusion in April 2021 issue.
Phylogenetic placement of Cailliella praerupticola (Melastomataceae), a rare, monospecific lineage from Guinea, West Africa

Abstract: The genus Cailliella includes a single species, C. praerupticola, restricted to a sandstone plateau region in Coyah, Dubreka, Forécariah and Kindia Préfectures, Guinea, West Africa. Until its rediscovery in 2016, this species was known only by the type gathering made in 1937. We provide an emended description, photographs of living material, locality and habitat data, and a discussion of its conservation status. We further designate a lectotype for C. praerupticola. On the basis of morphology, Cailliella had been considered an isolated member of the tribe Melastomateae, possibly sister to Melastomastrum. We tested the classification of Cailliella by means of a phylogenetic analysis of C. praerupticola and 40 other samples of African Melastomateae, based on molecular sequence data from the external and internal transcribed spacer regions of nuclear ribosomal DNA (nrETS and nrITS) as well as the ndhF, psbK-psbL and accD-psaI regions of chloroplast DNA. Our results corroborate the placement of Cailliella in the Melastomateae, where it was resolved as sister to a clade consisting of both Melastomastrum and Tristemma. The hypothesis of C. praerupticola being an evolutionary relict or “palaeoendemic” is supported by our findings. Citation: Veranso-Libalah M. C. , Stone R. D. , Haba P. M. , Magassouba S. , Kadereit G. & Burgt X. M. van der 2021: Phylogenetic placement of Cailliella praerupticola (Melastomataceae), a rare, monospecific lineage from Guinea, West Africa. – Willdenowia 51: 47–56. Version of record first published online on 9 March 2021 ahead of inclusion in April 2021 issue.
Tapeout of a RISC-V crypto chip with hardware trojans: a case-study on trojan design and pre-silicon detectability

This paper presents design and integration of four hardware Trojans (HTs) into a post-quantum-crypto-enhanced RISC-V micro-controller, which was taped-out in September 2020. We cover multiple HTs ranging from a simple denial-of-service HT to a side-channel HT transmitting arbitrary information to external observers. For each HT, we give estimations of the detectability by the microcontroller-integration team using design tools or by simulation. We conclude that some HTs are easily detected by design-tool warnings. Other powerful HTs, modifying software control flow, cause little disturbance, but require covert executable code modifications. With this work, we strengthen awareness for HT risks and present a realistic testing device for HT detection tools.
DFShards: effective construction of MRCs online for non-stack algorithms

The Miss Ratio Curve (MRC) describes the cache miss ratio as a function of the cache size. It has various shapes that represent the data access behaviors of workloads in the cache. MRC is an effective tool to guide cache partitioning, but its real-time construction is challenging. Miniature Simulation is a novel approach that constructs MRCs for non-stack algorithms in real time, via feeding a small number of sample references to multiple mini caches simultaneously to get the miss ratios. However, while using the Miniature Simulation, the size and number of mini-caches are difficult to set before the program runs. First, it may set too many mini-caches and cause repeated simulations. Second, it may miss some important cache sizes and consequently construct a less precise shape of MRC and result in incorrect cache partitioning. To address this problem, we propose DFShards, an adaptive cache shards (mini-caches) configuration approach based on program access patterns. The key idea is to dynamically adjust the configuration of the cache shards, including the number of the total cache shards and the size of each cache shard, based on the access behaviors to reflect changes in workload to build an precise MRC, thereby achieving better cache partitioning and overall performance. Our extensive experiments show that DFShards can construct precise MRCs in real-time during program running. Compared to the state-of-the-art approaches, it can save up to 47% of the cache space for MRC constructions while increasing the cache hit ratio by up to 17%.
Power-efficient Spike Sorting Scheme Using Analog Spiking Neural Network Classifier

The method to map the neural signals to the neuron from which it originates is spike sorting. A low-power spike sorting system is presented for a neural implant device. The spike sorter constitutes a two-step trainer module that is shared by the signal acquisition channel associated with multiple electrodes. A low-power Spiking Neural Network (SNN) module is responsible for assigning the spike class. The two-step shared supervised on-chip training module is presented for improved training accuracy for the SNN. Post implant, the relatively power-hungry training module can be activated conditionally based on a statistics-driven retraining algorithm that allows on the fly training and adaptation. A low-power analog implementation for the SNN classifier is proposed based on resistive crossbar memory exploiting its approximate computing nature. Owing to the direct mapping of SNN functionality using physical characteristics of devices, the analog mode implementation can achieve ∼21 × lower power than its fully digital counterpart. We also incorporate the effect of device variation in the training process to suppress the impact of inevitable inaccuracies in such resistive crossbar devices on the classification accuracy. A variation-aware, digitally calibrated analog front-end is also presented, which consumes less than ∼50 nW power and interfaces with the digital training module as well as the analog SNN spike sorting module. Hence, the proposed scheme is a low-power, variation-tolerant, adaptive, digitally trained, all-analog spike sorter device, applicable to implantable and wearable multichannel brain-machine interfaces.
A comparative survey of open-source application-class RISC-V processor implementations

The numerous emerging implementations of RISC-V processors and frameworks underline the success of this Instruction Set Architecture (ISA) specification. The free and open source character of many implementations facilitates their adoption in academic and commercial projects. As yet it is not easy to say which implementation fits best for a system with given requirements such as processing performance or power consumption. With varying backgrounds and histories, the developed RISC-V processors are very different from each other. Comparisons are difficult, because results are reported for arbitrary technologies and configuration settings. Scaling factors are used to draw comparisons, but this gives only rough estimates. In order to give more substantiated results, this paper compares the most prominent open-source application-class RISC-V projects by running identical benchmarks on identical platforms with defined configuration settings. The Rocket, BOOM, CVA6, and SHAKTI C-Class implementations are evaluated for processing performance, area and resource utilization, power consumption as well as efficiency. Results are presented for the Xilinx Virtex UltraScale+ family and GlobalFoundries 22FDX ASIC technology.
Ultra-compact binary neural networks for human activity recognition on RISC-V processors

Human Activity Recognition (HAR) is a relevant inference task in many mobile applications. State-of-the-art HAR at the edge is typically achieved with lightweight machine learning models such as decision trees and Random Forests (RFs), whereas deep learning is less common due to its high computational complexity. In this work, we propose a novel implementation of HAR based on deep neural networks, and precisely on Binary Neural Networks (BNNs), targeting low-power general purpose processors with a RISC-V instruction set. BNNs yield very small memory footprints and low inference complexity, thanks to the replacement of arithmetic operations with bit-wise ones. However, existing BNN implementations on general purpose processors impose constraints tailored to complex computer vision tasks, which result in over-parametrized models for simpler problems like HAR. Therefore, we also introduce a new BNN inference library, which targets ultra-compact models explicitly. With experiments on a single-core RISC-V processor, we show that BNNs trained on two HAR datasets obtain higher classification accuracy compared to a state-of-the-art baseline based on RFs. Furthermore, our BNN reaches the same accuracy of a RF with either less memory (up to 91%) or more energy-efficiency (up to 70%), depending on the complexity of the features extracted by the RF.
On resilience of security-oriented error detecting architectures against power attacks: a theoretical analysis

It has been previously shown that hardware implementation of fault attack countermeasures based on error-detecting codes (EDCs) can make the circuit more vulnerable to power analysis attacks. We revisit this finding and show that the hypothesis space can grow significantly when a state-of-the-art security-oriented robust EDC is properly crafted. We use the Roth-Karp decomposition as an analytical tool to prove that by a simple re-ordering of the EDC's bits, the number of extra bits needed to formulate the hypotheses becomes so large that power analysis (that tries to exploit additional information from the redundant bits) is rendered infeasible.
Architecting more than Moore: wireless plasticity for massive heterogeneous computer architectures (WiPLASH)

This paper presents the research directions pursued by the WiPLASH European project, pioneering on-chip wireless communications as a disruptive enabler towards next-generation computing systems for artificial intelligence (AI). We illustrate the holistic approach driving our research efforts, which encompass expertises and abstraction levels ranging from physical design of embedded graphene antennas to system-level evaluation of wirelessly-communicating heterogeneous systems.
Real-time Subsurface Control Variates

Real-time adaptive sampling is a new technique recently proposed for efficient importance sampling in realtime Monte Carlo sampling in subsurface scattering. It adaptively places samples based on variance tracking to help escape the uncanny valley of subsurface rendering. However, the occasional performance drop due to temporal lighting dynamics (e.g., guns or lights turning on and off) could hinder adoption in games or other applications where smooth high frame rate is preferred. In this paper we propose a novel usage of Control Variates (CV) in the sample domain instead of shading domain to maintain a consistent low pass time. Our algorithm seamlessly reduces to diffuse with zero scattering samples for sub-pixel scattering. We propose a novel joint-optimization algorithm for sample count and CV coefficient estimation. The main enabler is our novel time-variant covariance updating method that helps remove the effect of recent temporal dynamics from variance tracking. Since bandwidth is critical in real-time rendering, a solution without adding any extra textures is also provided.
Synthesizing Indoor Scene Layouts in Complicated Architecture Using Dynamic Convolution Networks

Synthesizing indoor scene layouts is challenging and critical, especially for digital design and gaming entertainment. Although there has been significant research on the indoor layout synthesis of rectangular-shaped or L-shaped architecture, there is little known about synthesizing plausible layouts for more complicated indoor architecture with both geometric and semantic information of indoor architecture being fully considered. In this paper, we propose an effective and novel framework to synthesize plausible indoor layouts in various and complicated architecture. The given indoor architecture is first encoded to our proposed representation, called InAiR, based on its geometric and semantic information. The indoor objects are grouped and then arranged by functional blocks, represented by oriented bounding boxes, using dynamic convolution networks based on their functionality and human activities. Through comparisons with other approaches as well as comparative user studies, we find that our generated indoor scene layouts for diverse, complicated indoor architecture are visually indistinguishable, which reach state-of-the-art performance.
Guided Visibility Sampling++

Visibility computation is a common problem in the field of computer graphics. Examples include occlusion culling, where parts of the scene are culled away, or global illumination simulations, which are based on the mutual visibility of pairs of points to calculate lighting. In this paper, an aggressive from-region visibility technique called Guided Visibility Sampling++ (GVS++) is presented. The proposed technique improves the Guided Visibility Sampling algorithm through improved sampling strategies, thus achieving low error rates on various scenes, and being over four orders of magnitude faster than the original CPU-based Guided Visibility Sampling implementation. We present sampling strategies that adaptively compute sample locations and use ray casting to determine a set of triangles visible from a flat or volumetric rectangular region in space. This set is called a potentially visible set (PVS). Based on initial random sampling, subsequent exploration phases progressively grow an intermediate solution. A termination criterion is used to terminate the PVS search. A modern implementation using the Vulkan graphics API and RTX ray tracing is discussed. Furthermore, we show optimizations that allow for an implementation that is over 20 times faster than a naive implementation.
Toward better ways of measuring school climate

As states consider non-academic measures of school quality for their accountability plans under the Every Student Succeeds Act, few are choosing to focus on the school climate in which students learn. That’s not a surprise, given concerns about the validity and usefulness of existing climate surveys. However, the authors argue, a recent study suggests that by making a couple of modest changes to their data collection and analysis, states can significantly improve their measurement of school climate. Doing so is more than worth the effort, they add, as it will create strong incentives for schools to improve their learning environment.
DigitalCommons@UNO DigitalCommons@UNO

As police agencies in the United States suffer declining applications and struggle to recruit women, the National Institute of Justice has identified workforce development as a priority research area. To recruit more effectively, we must understand what attracts people to policing and what deters them. We surveyed officers in two Midwestern police departments ( n = 832) about entry motivations and concerns and examined gender differences. Serve/protect motivations were most important for men and women, though women rated the category significantly higher. Women and non-White officers rated legacy motives higher than did males and White officers. Women reported more concerns overall and scored higher on job demands and acceptance concerns; officers of color also reported more acceptance concerns than White officers. The largest gender differences were associated with gender-related obstacles and stereotypes (e.g., discrimination; being taken seriously; physical demands), indicating recruitment reform necessarily includes improving systemic issues.
Structural Gendered Racism Revealed in Pandemic Times: Intersectional Approaches to Understanding Race and Gender Health Inequities in COVID-19

The pandemic reveals; the novel coronavirus (COVID-19) pandemic has brought the historically rooted inequities of our society to the forefront. We argue that an intersectional analysis is needed to further help peel back the veil that the pandemic has begun to reveal. We identify structural gendered racism—the totality of interconnectedness between structural racism and structural sexism in shaping race and gender inequities—as a root cause of health problems among Black women and other women of color, which has been amplified during the pandemic. We show that women of color occupy disadvantaged positions within households, occupations, and health care institutions, and therefore face heightened risk for COVID-19 and lowered resources for mitigating the impact of the deadly virus. Intersectional analyses and solutions must be centered to also reveal, we hope, a new way forward.
A multiplex antigen microarray for simultaneous IgG and IgM detection against SARS‐CoV‐2 reveals higher seroprevalence than reported

The surge of SARS‐CoV‐2 has challenged health systems worldwide and efficient tests to detect viral particles, as well as antibodies generated against them, are needed. Specificity, sensitivity, promptness or scalability are the main parameters to estimate the final performance, but rarely all of them match in a single test. We have developed SCOVAM, a protein microarray with several viral antigens (spike, nucleocapsid, main protease Nsp5) as capturing probes in a fluorescence immunoassay for COVID‐19 serological testing. SCOVAM depicts IgG and IgM antibody responses against each of these proteins of 22 individuals in a single microscope slide. It detects specific IgM (0.094 μg ml‐1) and IgG (~0.017 μg ml‐1) and is scalable and cost‐effective. We validated SCOVAM by comparing with a widely used chemiluminescent commercial serological test (n = 742). SCOVAM showed twice the sensitivity and allowed following seroconversion in a single assay. By analysing the prevalence 4 months later in a subset of 76 positive sera, we still detected 93.42% of positives, almost doubling the detection of the commercial assay. The higher sensitivity of SCOVAM is especially relevant to screen sera for convalescent plasma‐based treatments, high‐throughput antibody response monitoring after vaccination or evaluation of vaccine efficiency.
Therapeutic targeting of SLC6A8 creatine transporter inhibits KRAS mutant and wildtype colon cancer and modulates human creatine levels

Colorectal cancer (CRC) is a leading cause of cancer mortality. Creatine metabolism was previously shown to critically regulate colon cancer progression. We report that RGX-202, an oral small-molecule SLC6A8 creatine transporter inhibitor, robustly inhibits creatine import in vitro and in vivo, reduces intracellular phosphocreatine and ATP levels and induces tumor cell apoptosis in CRC. RGX-202 suppressed tumor growth across KRAS wild-type and KRAS mutant xenograft, syngeneic and patient-derived xenograft colorectal cancers. Anti-tumor efficacy correlated with tumoral expression of creatine kinase B. Combining RGX-202 with 5- fluorouracil or the DHODH inhibitor leflunomide caused regressions of multiple colorectal xenograft and PDX tumors of distinct mutational backgrounds. RGX-202 also perturbed creatine metabolism in metastatic CRC patients enrolled in a Phase-1 trial, mirroring pharmacodynamic effects on creatine metabolism observed in mice. This is, to our knowledge, the first demonstration of pre-clinical and human pharmacodynamic activity for creatine metabolism targeting in oncology, revealing a critical target for CRC.
Senolytics and the compression of late-life mortality

Senescent cells play an important role in mammalian ageing and in the etiology of age-related diseases. Treatment of mice with senolytics – drugs that selectively remove senescent cells – causes an extension of median lifespan but has little effect on maximum lifespan. Postponement of some mortality to later ages, without a corresponding increase in maximum mortality, can be termed ‘compression of mortality’. When we fit the standard Gompertz mortality model to the survival data following senolytic treatment, we find an increase in the slope parameter, commonly described as the ‘actuarial ageing rate’. These observations raise important questions about the actions of senolytic treatments and their effects on health and survival, which are not yet sufficiently understood. To explore how the survival data from senolytics experiments might be explained, we combine recent exploration of the evolutionary basis of cellular senescence with theoretical consideration of the molecular processes that might be involved. We perform numerical simulations of senescent cell accumulation and senolytic treatment in an ageing population. The simulations suggest that while senolytics diminish the burden of senescent cells, they may also impair the general repair capacity of the organism, leading to a faster accumulation post-treatment of new senescent cells. Our results suggest a framework to address the benefits and possible side effects of senolytic therapies, with the potential to aid the design of optimal treatment regimens.
Variations in soil nutrient dynamics and bacterial communities in long-term tea monoculture production systems

Long-term monoculture agriculture systems could lead to soil degradation and yield decline. The ways in which soil microbiotas interact with one another, particularly in response to long-term tea monoculture systems are currently unclear. In this study, through the comparison of three independent tea plantations across eastern China composed of varying stand ages (from 3 years to 90 years after conversion from forest), we found that long-term tea monoculture led to significant increases in soil total organic carbon (TOC) and microbial nitrogen (MBN). Additionally, the structure, function and co-occurrence network of soil microbial communities were investigated by pyrosequencing 16S rRNA genes. The pyrosequencing analysis revealed that structures and functions of soil bacterial communities were significantly affected by different stand ages of tea plantations, but sampling sites and land-use conversion (from forest to tea plantation) still outcompeted stand age to control the diversity and structure of soil bacterial communities. Further RDA analysis revealed that the C and N availability improvement in tea plantation soils led to variation of structure and function in soil microbial communities. Moreover, co-occurrence network analysis of soil bacterial communities also demonstrated that interactions among soil bacteria taxa were strengthened with the increasing stand age of respective tea stands. Overall, this study provides a comprehensive understanding of the impact of long-term monoculture stand age on soil nutrient dynamics and bacterial communities in tea production.