Miracle-dz/UniFAR-FTUs huggingface dataset:
Crop rotation and tillage system effects on weed seedbanks

Abstract We characterized the size and species composition of the weed seedbank after 35 yr of continuous crop rotation and tillage system treatments at two locations in Ohio. Spring seedbanks were monitored during 1997, 1998, and 1999 in continuous corn (CCC), corn–soybean (CS), and corn–oats–hay (COH) rotations in moldboard plow (MP), chisel plow (CP), and no-tillage (NT) plots where the same herbicide was used for a given crop each growing season. There were 47 species at Wooster and 45 species at Hoytville, with 37 species occurring at both locations in all 3 yr. Crop rotation was a more important determinant of seed density than was tillage system. Seed density was highest in NT and generally declined as tillage intensity increased. Seeds accumulated near the surface (0 to 5 cm) in NT but were uniformly distributed with depth in other tillage systems. At both locations there was a significant interaction between tillage and rotation for estimates of the total seed density. Seed density was highest in NT-CCC, with 26,850 seeds m−2 at Wooster and 8,680 seeds m−2 at Hoytville. At Wooster total seed density in CCC plots was 45 and 60% lower than in COH plots for CP and MP. In NT the total seed density was 40% greater in CCC than in COH. At Hoytville total seed density in CCC plots was 72% lower than in COH plots that were CP or MP, whereas seed density was 45% higher in CCC than in COH plots that were in an NT system. There were more significant differences in seedbank density for any given species for crop rotation than for tillage treatments. Seed densities of three broadleaves (shepherd's-purse, Pennsylvania smartweed, and corn speedwell) at Wooster and four broadleaves (yellow woodsorrel, redroot pigweed, Pennsylvania smartweed, and spotted spurge) at Hoytville were more abundant in COH (140 to 630 seeds m−2) than in CS (10 to 270 seeds m−2) or CCC (< 1 to 60 seeds m−2), regardless of the tillage system. At both locations Pennsylvania smartweed seeds were more abundant in COH (260 and 630 seeds m−2) than in other rotations (10 to 20 seeds m−2). Relative importance (RI) values, based on relative density and relative frequency of each species, were lower in CS than in CCC for common lambsquarters and five other weeds at Wooster; RI of giant foxtail was 80% lower in COH than in CCC at Hoytville. The data show how species composition and abundance change in response to crop and soil management. The results can help to determine how complex plant communities are “assembled” from a pool of species by specific constraints or filters. Nomenclature: Redroot pigweed, Amaranthus retroflexus L. AMARE; shepherd's-purse, Capsella bursa-pastoris (L.) Medicus CAPSA; common lambsquarters, Chenopodium album L. CHEAL; spotted spurge, Euphorbia maculata L. EPHMA; yellow woodsorrel, Oxalis stricta L. OXAST; Pennsylvania smartweed, Polygonum pensylvanicum L. POLPY; giant foxtail, Setaria faberi L. SETFA; corn speedwell, Veronica arvensis L. VERAR; oats, Avena sativa L. ; soybean, Glycine max (L.) Merr. ; corn, Zea mays L.
Reference study of IFC software support: The GeoBIM benchmark 2019—Part I

Industry Foundation Classes (IFC), the buildingSMART open standard for BIM, is underused with respect to its promising potential, since, according to the experience of practitioners and researchers working with BIM, issues in the standard’s implementation and use prevent its effective use. Nevertheless, a systematic investigation of these issues has never been carried out, and there is thus insufficient evidence for tackling the problems. The GeoBIM benchmark project is aimed at finding such evidence by involving external volunteers, reporting on various aspects of the behavior of tools (geometry, semantics, georeferencing, functionalities), analyzed and described in this article. Interestingly, different IFC software programs with the same standardized data sets yield inconsistent results, with few detectable common patterns, and significant issues are found in their support of the standard, probably due to the very high complexity of the standard data model. A companion article (Part II) describes the results of the benchmark related to CityGML, the counterpart of IFC within geoinformation.
Sparse Blind Demixing for Low-latency Signal Recovery in Massive Iot Connectivity

Internet-of-Things (IoT) networks are envisioned to typically include a massive number of devices with sporadic and low-latency uplink service needs. This paper presents a blind demixing approach to support the data recovery of multiple simultaneous and unscheduled device transmissions without a priori channel state information (CSI). The proposed joint receiver leverages the group sparse bilinear characteristics of the underlying problem that involves active device detection and data recovery. We exploit the manifold geometry of rank-one matrices in the lifted bilinear equation and apply smoothed ℓ1/ℓ2-norm to induce the group sparsity for active device detection. We further develop a smoothed Riemannian algorithm to solve the sparse blind demixing optimization problem. Numerical results demonstrate the algorithmic advantage and desirable performance of the proposed algorithm.
The role of calcium channel blockers in the treatment of diabetic nephropathy

Diabetic nephropathy (DNP) is a chronic renal disease (CRD) and a major cause of illness and premature death in people with diabetes mellitus (DM). It is the single most important cause of end-stage renal disease in the Western world and accounts for more than a quarter of all end-stage renal diseases. This article reviews the current development in DNP and the therapeutic challenge with particular reference to the role of calcium channel blockers. Moreover, renal ischaemia hastens the progression of DNP. Diltiazem and amlodipine have a tendency to reverse the changed parameters toward normal values but do not affect the biochemical parameters. Generally speaking, diltiazem is better than amlodipine in reversing biochemical and histopathological changes produced by DNP, and captopril reverses most of the changed parameters with the exception of the histopathological changes. These agents have nephroprotective properties and delay the progression of DNP.
BeeSwarm: Enabling Scalability Tests in Continuous Integration

Testing is one of the most important steps in software development. It ensures the quality of software. Continuous Integration (CI) is a widely used testing system that can report software quality to the developer in a timely manner during the development progress. Performance, especially scalability, is another key factor for High Performance Computing (HPC) applications. Though there are many applications and tools to profile the performance of HPC applications, none of them are integrated into the continuous integration. On the other hand, no current continuous integration tools provide easy-to-use scalability test capabilities. In this work, we propose BeeSwarm, a scalability test system that can be easily applied to the current CI test environment enabling scalability test capability for HPC developers. As a showcase, BeeSwarm is integrated into Travis CI and GitLab CI to execute the scalability test workflow on Chameleon cloud.
Extended X-ray emission from the z = 4.26 radio galaxy 4C 63.20

We report on deep Chandra X-ray Telescope imaging observations of 4C 63.20, one of the few known radio galaxies at z>3.5. The X-ray counterpart is resolved into a core plus two off-nuclear sources that (combined) account for close to 30% of the total X-ray flux. Their morphology and orientation are consistent with a diffuse, lobe-like nature, albeit compact hotspots cannot be ruled out. The broadband spectral energy distribution of 4C 63.20 can be reproduced with a jet model where the majority of the radio flux can be ascribed to synchrotron emission from the hotspots, whereas the (non-nuclear) X-ray emission is produced via Inverse Compton (IC) off of Cosmic Microwave Background (CMB) photons within the extended lobes. This scenario is broadly consistent with the expectation from highly magnetized lobes in a hotter CMB, and supports the view that IC/CMB may quench less extreme radio lobes at high redshifts.
Tight Approximations for Modular and Submodular Optimization with D-resource Multiple Knapsack Constraints

A multiple knapsack constraint over a set of items is defined by a set of bins of arbitrary capacities, and a weight for each of the items. An assignment for the constraint is an allocation of subsets of items to the bins, such that the total weight of items assigned to each bin is bounded by the bin capacity. We study modular (linear) and submodular maximization problems subject to a constant number of (i.e., $d$-resource) multiple knapsack constraints, in which a solution is a subset of items, along with an assignment of the selected items for each of the $d$ multiple knapsack constraints. Our results include a polynomial time approximation scheme for modular maximization with a constant number of multiple knapsack constraints and a matroid constraint, thus generalizing the best known results for the classic multiple knapsack problem as well as d-dimensional knapsack, for any $d \geq 2$. We further obtain a tight $(1-e^{-1}-\epsilon)$-approximation for monotone submodular optimization subject to a constant number of multiple knapsack constraints and a matroid constraint, and a $(0.385-\epsilon)$-approximation for non-monotone submodular optimization subject to a constant number of multiple knapsack constraints. At the heart of our algorithms lies a novel representation of a multiple knapsack constraint as a polytope. We consider this key tool as a main technical contribution of this paper.
Conformer-Kernel with Query Term Independence for Document Retrieval

The Transformer-Kernel (TK) model has demonstrated strong reranking performance on the TREC Deep Learning benchmark---and can be considered to be an efficient (but slightly less effective) alternative to BERT-based ranking models. In this work, we extend the TK architecture to the full retrieval setting by incorporating the query term independence assumption. Furthermore, to reduce the memory complexity of the Transformer layers with respect to the input sequence length, we propose a new Conformer layer. We show that the Conformer's GPU memory requirement scales linearly with input sequence length, making it a more viable option when ranking long documents. Finally, we demonstrate that incorporating explicit term matching signal into the model can be particularly useful in the full retrieval setting. We present preliminary results from our work in this paper.
TCIM: Triangle Counting Acceleration With Processing-In-MRAM Architecture

Triangle counting (TC) is a fundamental problem in graph analysis and has found numerous applications, which motivates many TC acceleration solutions in the traditional computing platforms like GPU and FPGA. However, these approaches suffer from the bandwidth bottleneck because TC calculation involves a large amount of data transfers. In this paper, we propose to overcome this challenge by designing a TC accelerator utilizing the emerging processing-in-MRAM (PIM) architecture. The true innovation behind our approach is a novel method to perform TC with bitwise logic operations (such as AND), instead of the traditional approaches such as matrix computations. This enables the efficient in-memory implementations of TC computation, which we demonstrate in this paper with computational Spin-Transfer Torque Magnetic RAM (STT-MRAM) arrays. Furthermore, we develop customized graph slicing and mapping techniques to speed up the computation and reduce the energy consumption. We use a device-to-architecture co-simulation framework to validate our proposed TC accelerator. The results show that our data mapping strategy could reduce 99.99% of the computation and 72% of the memory WRITE operations. Compared with the existing GPU or FPGA accelerators, our in-memory accelerator achieves speedups of 9× and 23.4×, respectively, and a 20.6× energy efficiency improvement over the FPGA accelerator.
RAPD VARIATION OF LATE-MATURING SORGHUM (Sorghum bicolor (L.) MOENCH) LANDRACES FROM ETHIOPIA

A study on the extent and pattern of genetic variability in late-maturing sorghum [ Sorghum bicolor (L.) Moench] landraces collected from the Wello and Hararge areas of Ethiopia was conducted using random amplified polymorphic DNA (RAPD) markers for 70 individuals representing 14 populations. Four oligonucleotide primers generated a total of 55 polymorphic bands with 13–19 bands per primer and a mean of 16 bands across the 70 individuals. The value of the Shannon diversity index among the populations (0.26) and between the two regions (0.24) was low to moderate, despite the high degree of polymorphic bands per primer. The mean genetic distance (0.25) between the populations was found to be low. The low genetic variation may be due to the reduced population size of late-maturing sorghum landraces in the two regions of Ethiopia because of farmers’ decisions in the process of planting, managing, harvesting and processing their crops. Partitioning of the genetic variation into variation between and within the.
Prognostic and clinicopathological significance of systemic immune-inflammation index in colorectal cancer: a meta-analysis

Background: Previous studies on the systemic immune-inflammation index (SII), which is based on platelet, neutrophil and lymphocyte counts, as a prognostic marker in patients with colorectal cancer (CRC) yielded inconsistent results. The aim of this study was to evaluate the prognostic and clinicopathological role of SII in CRC via meta-analysis. Methods: A comprehensive literature survey was performed on PubMed, Web of Science, Embase and the Cochrane Library databases to include studies published up to 6 April 2020. Pooled hazard ratios (HRs) and odds ratios (ORs) with 95% confidence intervals (CIs) were computed to estimate the prognostic and clinicopathological value of SII in CRC. Results: A total of 12 studies published between 2016 and 2019 were included in our meta-analysis. The combined analysis showed that high SII levels were significantly associated with worse overall survival (OS; HR = 1.61, 95% CI = 1.21–2.13, p = 0.001) and progression-free survival (HR = 1.74, 95% CI = 1.26–2.39, p = 0.001) in CRC. Moreover, elevated SII was also correlated with poor tumor differentiation (OR = 1.60, 95% CI = 1.27–2.02, p < 0.001), presence of distant metastasis (OR = 2.27, 95% CI = 1.10–4.67, p = 0.026), ECOG PS of 1–2 (OR = 1.98, 95% CI = 1.39–2.84, p < 0.001) and tumor size ⩾5 cm (OR = 1.49, 95% CI = 1.18–1.88, p = 0.001). However, high SII was not significantly associated with sex, tumor location, lymph node metastasis, or age in patients with CRC. Conclusion: Our meta-analysis indicated that high SII levels predicted poor prognosis in CRC. In addition, an elevated SII was also associated with clinical factors, implying higher malignancy of the disease.
BSD-GAN: Branched Generative Adversarial Network for Scale-Disentangled Representation Learning and Image Synthesis

We introduce BSD-GAN, a novel multi-branch and scale-disentangled training method which enables unconditional Generative Adversarial Networks (GANs) to learn image representations at multiple scales, benefiting a wide range of generation and editing tasks. The key feature of BSD-GAN is that it is trained in multiple branches, progressively covering both the breadth and depth of the network, as resolutions of the training images increase to reveal finer-scale features. Specifically, each noise vector, as input to the generator network of BSD-GAN, is deliberately split into several sub-vectors, each corresponding to, and is trained to learn, image representations at a particular scale. During training, we progressively “de-freeze” the sub-vectors, one at a time, as a new set of higher-resolution images is employed for training and more network layers are added. A consequence of such an explicit sub-vector designation is that we can directly manipulate and even combine latent (sub-vector) codes which model different feature scales. Extensive experiments demonstrate the effectiveness of our training method in scale-disentangled learning of image representations and synthesis of novel image contents, without any extra labels and without compromising quality of the synthesized high-resolution images. We further demonstrate several image generation and manipulation applications enabled or improved by BSD-GAN.
Defining Outcomes Following Distal Radius Fractures: Correlation of Function, Pain, and Hand Therapy Utilization

Background Distal radius fractures (DRF) is one of the most common fractures in clinical practice. Our objective was to study the role of early hand therapy and its impact on pain and return to daily activities. Methods The charts of patients with DRFs seen between January 2016 and November 2017 in the Hand Center of Mayo Clinic Florida were reviewed retrospectively. Forty-nine patients with DRFs who met inclusion criteria were included in the analysis. The variables collected included: age, gender, side of the fracture, surgery vs non-surgery, time to start hand therapy, number of visits, shortened disabilities of the arm, shoulder, and hand (QuickDASH) initial and discharge scores, and visual analog scale (VAS) initial and discharge. Results The patients’ mean age was 67.90 years, (standard deviation (SD) 14.54), 38 (77.6%) were female, 28 (57.1%) had a right DRF, 21 (42.9%) had a left DRF, 38 (77.6%) had no surgery, 11 (22.4%) had surgery. The mean time from fracture to therapy is 32.41, (SD 24.13) days, and the mean total number of visits is 6.20 (SD 3.49). We noticed a statistically significant difference between the initial QuickDASH (59.27, SD 16.93) compared to the discharge QuickDASH (24.08, SD 12.77) (P-value <.001); and initial VAS (3.57, SD 1.71) with a discharge VAS (1.33, SD 0.97) (P-value <.001). Conclusion This retrospective study found a statistically significant reduction in the QuickDASH and VAS scores after six hand therapy visits. The results suggest that early rehabilitation interventions lead to improvements in pain and return to daily activity following DRF.
Perianal basal cell carcinoma: a common cancer in an uncommon location

Abstract This is a case of a perianal basal cell carcinoma, a common skin cancer in an unusual location. Our patient is a 67-year-old male with a perianal lesion. He first noticed this painless lesion 5 years prior to presentation and was having fecal incontinence and weight loss. He had a fully encompassing ulcerated lesion involving the entirety of the anal margin. We performed a biopsy that returned on pathology as a basal cell carcinoma. Due to the size of the lesion and his current nutritional status, it was determined to be unresectable. We were able to provide him with a diverting colostomy to address his incontinence and this allowed the patient to recover enough to undergo treatment with radiation (total of 5400 cGy). To our knowledge, this is the largest perianal basal cell carcinoma reported in the literature and an example of combining palliative surgery and radiation as a treatment option.
High Degree Atrioventricular Block in Non-ST-Segment Elevation Myocardial Infarction (NSTEMI) and Role of Early Revascularization

High degree atrioventricular block (HDAVB) is a rare complication of non-ST-elevation myocardial infarction (NSTEMI) and often requires pacemaker implantation. We describe a case of reversal of HDAVB in a patient with dual atrioventricular node blood supply following coronary revascularization and potential mechanisms of such complications.
Anatomical Variations That Can Lead to Spine Surgery at the Wrong Level: Part I, Cervical Spine

Spine surgery at the wrong level is an adversity that many spine surgeons will encounter in their career, and it falls under the wrong-site surgery sentinel events reporting system. The cervical spine is the second most common location in the spine at which surgery is performed at the wrong level. Anatomical variations of the cervical spine are one of the most important incriminating risk factors. These anomalies include craniocervical junction abnormalities, cervical ribs, hemivertebrae, and block/fused vertebrae. In addition, patient characteristics, such as tumors, infection, previous cervical spine surgery, obesity, and osteoporosis, play an important role in the development of cervical surgery at the wrong level. These were described, and several effective techniques to prevent this error were provided. A thorough review of the English-language literature was performed in the database PubMed between 1981 and 2019 to review and summarize these risk factors. Compulsive attention to these factors is essential to ensure patient safety. Therefore, the surgeon must carefully review the patient’s anatomy and characteristics through imaging and collaborate with radiologists to reduce the likelihood of performing cervical spine surgery at the wrong level.
Nanoparticle-Based Drug Delivery System: A Patient-Friendly Chemotherapy for Oncology

Chemotherapy is widely used to treat cancer. The toxic effect of conventional chemotherapeutic drugs on healthy cells leads to serious toxic and side effects of conventional chemotherapy. The application of nanotechnology in tumor chemotherapy can increase the specificity of anticancer agents, increase the killing effect of tumors, and reduce toxic and side effects. Currently, a variety of formulations based on nanoparticles (NPs) for delivering chemotherapeutic drugs have been put into clinical use, and several others are in the stage of development or clinical trials. In this review, after briefly introducing current cancer chemotherapeutic methods and their limitations, we describe the clinical applications and advantages and disadvantages of several different types of NPs-based chemotherapeutic agents. We have summarized a lot of information in tables and figures related to the delivery of chemotherapeutic drugs based on NPs and the design of NPs with active targeting capabilities.
Photothermal-assisted antibacterial application of graphene oxide-Ag nanocomposites against clinically isolated multi-drug resistant Escherichia coli

In the field of public health, treatment of multidrug-resistant (MDR) bacterial infection is a great challenge. Herein, we provide a solution to this problem with the use of graphene oxide-silver (GO-Ag) nanocomposites as antibacterial agent. Following established protocols, silver nanoparticles were grown on graphene oxide sheets. Then, a series of in vitro studies were conducted to validate the antibacterial efficiency of the GO-Ag nanocomposites against clinical MDR Escherichia coli (E. coli) strains. GO-Ag nanocomposites showed the highest antibacterial efficiency among tested antimicrobials (graphene oxide, silver nanoparticles, GO-Ag), and synergetic antibacterial effect was observed in GO-Ag nanocomposites treated group. Treatment with 14.0 µg ml−1 GO-Ag could greatly inhibit bacteria growth; remaining bacteria viabilities were 4.4% and 4.1% for MDR-1 and MDR-2 E. coli bacteria, respectively. In addition, with assistance of photothermal effect, effective sterilization could be achieved using GO-Ag nanocomposites as low as 7.0 µg ml−1. Fluorescence imaging and morphology characterization uncovered that bacteria integrity was disrupted after GO-Ag nanocomposites treatment. Cytotoxicity results of GO-Ag using human-derived cell lines (HEK 293T, Hep G2) suggested more than 80% viability remained at 7.0 µg ml−1. All the results proved that GO-Ag nanocomposites are efficient antibacterial agent against multidrug-resistant E. coli.