Miracle-dz/UniFAR-FTUs huggingface dataset:
A single synthetic lipid antigen for improved serological diagnosis of Buruli ulcer

SETTING: The diagnosis of Buruli ulcer (BU) is frequently made by experienced health workers in rural regions. This leads to long turnaround times to confirm the diagnosis as it requires specialised laboratory infrastructure to perform confirmatory testing. BACKGROUND: Given the lack of success with protein antigens to detect BU in human sera, the aim of this study was to evaluate a range of single synthetic lipid antigens using an enzyme-linked immunosorbent assay (ELISA). The ELISA system used was initially developed to detect TB using single synthetic lipid antigens. METHODS: Thirty polymerase chain reaction (PCR) positive BU samples and 30 PCR-negative healthy contact samples collected from Asante Akim North and Ahafo Ano North Districts, Ghana, that are endemic for BU between 2013 and 2016 were used to evaluate the synthetic lipid antigen ELISA. A Quantikine ELISA was also conducted on a randomly blinded sub-set of 30 samples. RESULTS: The synthetic lipid ELISA evaluated here outperforms all other ELISA tests using protein antigens to detect BU to date and has shown potential as a fast (2 h) test for BU which may be adapted for use at the point of care. A sensitivity of 63% and specificity of 80% was observed for 30 BU-positive and 30 BU-negative samples, with significantly reduced interleukin-8 (IL-8) levels in a subset of patients with BU. CONCLUSION: A single lipid was shown for the first time to have the ability to distinguish between PCR-positive BU and negative sera using ELISA. The low lipid antibody load detected may be a result of immune suppression caused by the presence of mycolactone in patients with BU, given that levels of IL-8 were significantly reduced in patients with BU compared to the control serum samples.
Bilateral Cortical-sparing Adrenalectomy for the Treatment of Bilateral Aldosterone-producing Adenomas

Abstract Aldosterone-producing adenoma (APA) is 1 of the major subtypes of primary aldosteronism (PA). Although most APA occurs unilaterally, bilateral APAs have rarely been documented. Because of its rarity, optimal management of patients with bilateral APAs has not been established. Here, we report a case of bilateral APAs that was successfully treated with simultaneous bilateral cortical-sparing surgery. A 44-year-old Japanese woman was referred to us for the evaluation of PA. She had typical clinical characteristics of PA, including hypertension, hypokalemia, and high plasma aldosterone concentration with suppressed renin. She was diagnosed as having PA based on the results of confirmatory testing. Computed tomography revealed bilateral adrenal nodules with diameters of 17 and 10 mm on the right and left adrenal gland, respectively. Adrenal venous sampling indicated excess aldosterone production from bilateral adrenal lesions. She underwent simultaneous bilateral laparoscopic partial adrenalectomy that normalized her blood pressure and serum potassium levels. Aldosterone synthase immunohistochemistry on the resected adrenal tumor tissue confirmed the diagnosis of bilateral APAs. Long-term postsurgical follow-up data suggest cure of the disease without the need for glucocorticoid replacement therapy. Bilateral cortical-sparing adrenalectomy appears to be a viable treatment option at least for selected patients with bilateral APAs.
Ultra-high static magnetic field induces a change in the spectrum but not frequency of DNA spontaneous mutations in Arabidopsis thaliana

Biological effects of magnetic fields have been extensively studied in plants, microorganisms and animals, and applications of magnetic fields in regulation of plant growth and phytoprotection is a promising field in sustainable agriculture. However, the effect of magnetic fields especially ultra-high static magnetic field (UHSMF) on genomic stability is largely unclear. Here, we investigated the mutagenicity of 24.5, 30.5 and 33.0 T UHSMFs with the gradient of 150, 95 and 0 T/m, respectively, via whole genome sequencing. Our results showed that 1 h exposure of Arabidopsis dried seeds to UHSMFs has no significant effect on the average rate of DNA mutations including single nucleotide variations and InDels (insertions and deletions) in comparison with the control, but 33.0 T and 24.5 T treatments lead to a significant change in the rate of nucleotide transitions and InDels longer than 3 bp, respectively, suggesting that both strength and gradient of UHSMF impact molecular spectrum of DNA mutations. We also found that the decreased transition rate in UHSMF groups is correlated with the upstream flanking sequences of G and C mutation sites. Furthermore, the germination rate of seeds exposed to 24.5 T SMF with -150 T/m gradient showed a significant decrease at 24 hours after sowing. Overall, our data lay a basis for precisely assessing the potential risk of UHSMF on DNA stability, and for elucidating molecular mechanism underlying gradient SMF-regulated biological processes in the future.
Empirical analysis of agricultural production and climate change: a case study of Nigeria.

The study analyzes climate change and agricultural production in Nigeria. The specific objectives includes evaluation of the trend changes in climatic parameter in Nigeria; analyzing of the trend in agricultural production; and examination of the dimension and linkage between agricultural production and climatic change parameters in Nigeria. Time series data were employed for the study. The data was sourced from the Central Bank of Nigeria and National Bureau of Statistics. Descriptive statistics and granger causality test analysis were the analytical tools used. It was observed that there is continuous rise in output from 1987 to 2000 before it dropped in 2001. The rise can be seen as a result of many programs and policies of the government, such as the Lower Niger River Basin Development Authority (LNRBDA) and The Agricultural Development Program (ADP). Temperature, on the other hand, remains relatively constant and does not affect agricultural output. The Granger causality approach revealed that changes in rainfall (climatic parameter) positively affects agricultural production in Nigeria. It is, therefore, recommended that if agricultural production will be increased and sustained, irrigation is the most suitable mode of water provisions, which would have not have negative influence on the environment.
Observation of internal waves in the Andaman Sea by ERS SAR

The Andaman Sea of the Indian Ocean is known to be a site in the world's ocean where extraordinarily large internal solitons are encountered. Synthetic aperture radar (SAR) images of the ERS-1/2 satellites acquired by the receiving station in Singapore reveal that the large internal solitons previously detected by in-situ oceanographic measurements in the western approaches of the Strait of Malacca between Phuket (Thailand) and the northern coast of Sumatra (Indonesia) are generated at shallow banks in the western part of the Andaman Sea. When propagating onto the shelf of the Malayan Peninsula, their spatial separation decreases and their shape becomes irregular, but they remain solitons of depression.
Simulation of Temperature Extremes Over West Africa With MPAS

A large ensemble of 51 simulations with the Model for Prediction Across Scales (MPAS) has been applied to assess its ability to reproduce extreme temperatures and heat waves in the area of West Africa. With its global approach the model avoids transition errors influencing the performance of limited area climate models. The MPAS simulations were driven with sea surface temperature (SST) and sea ice extent as the only boundary condition. The results reveal moderate cold biases in the range from −0.6° to −0.9°C for the daily mean temperature and −1.2° to −2.0°C for the area mean of the daily maximum temperature. The bias in the number of tropical nights ranges from +3 to −10 days. An underestimation by up to 50% is also present regarding the number of summer days. The heat wave duration index is underestimated regionally by 10%–60%. MPAS simulations are generally closer to the reanalysis results than they are to the observational reference. The results from long term runs and from short term runs with selected SST years are similar. Shortcomings in the reproduction of the temperature and precipitation indices found in the present investigation indicate that the global MPAS approach does provide a fidelity similar to that of the regional climate models.
Impact of Comorbidities on the Risk of Death and Intensive Care Admission in COVID-19 Patients Hospitalized in a North-African Center

Background: The Coronavirus Disease 2019 (COVID-19) pandemic has posed significant challenges to global health, with variations in outcomes among affected individuals. Comorbidities are known to influence the prognosis of various diseases, but their specific impact on COVID-19 patients in North-African settings remains to be elucidated. To evaluate the risk of death and intensive care unit (ICU) transfer in patients with COVID-19 by stratifying on comorbidity status. Methods: Data of confirmed hospitalized patients at the university hospital of Kairouan (Tunisia) between September 2020 and August 2021 were collected. Composite endpoints including admission to an ICU or death were analysed. The risk of reaching the composite endpoint was compared according to the presence and number of comorbidities. Kaplan-Meir methods and Cox proportional hazards regression models were used to study the time to composite endpoint and estimate its prognostic factors. Results: A total of 866 patients were included. The mean age was 65 ± 14 years. Most common comorbidities were arterial hypertension (41.1%) and diabetes mellitus (31.2%). Overall, 316 (36.5%) patients reached the composite endpoint: 263(30.4%) patients died and 53 (6.1%) were admitted to the ICU. Cardiovascular disease and chronic renal failure were associated with higher risk of reaching the composite endpoints. Conclusion: COVID-19 patients with comorbidities were more likely to experience severe outcomes. Aggressive strategies to control the COVID-19 pandemic should target patients with chronic diseases, especially cardiovascular disease, and chronic renal failure, as a priority.
Investigating factors that influence the catch rates of northern stone crab (Lithodes maja)

Natural resources in marine and coastal areas are of crucial economic importance for the province of Newfoundland and Labrador. They sustain the livelihoods and natural heritage of communities, and provide stable income for current and future generations, provided they are well-managed. Not all fisheries in Newfoundland and Labrador are fully exploited, and there are ongoing efforts to establish new sustainable industries in the province. For commercial fisheries, management requires a detailed assessment of the gear used in the fishery to ensure a sustainable industry. Assessing the effectiveness of fishing gears, and understanding the extent of fishing gear impacts on marine ecosystems, can provide unique protection for aquatic habitats and help ensure sustainability of marine species. In this study, I assessed a potential fishing gear for use in a northern stone crab (Lithodes maja: Linnaeus, 1758) fishery, and investigated how abiotic factors - water current direction, hourly variation in water current direction, turbidity and interspecific interractions, influence catch rates of this species, with the aid of an underwater video-camera. Results demonstrate that the Norwegian two-door pot is effective in capturing this species, and catch rates of this species is partly influenced by water current directions, hourly variation in water current direction and interspecific interactions of species. Turbidity had no effect on the catch rates of northern stone crab. In addition, there was little or no impact of the pot on the sea bed, there were no cases of escape and discard mortality, ghost fishing, lost pot, or injury to both target or non-target species in the study. Conscientious fishery management strategies remain essential in the effort towards ensuring sustainable existing and emerging commercial fisheries, as well as the benthic ecosystem.
Space Microdatacenters

Earth observation (EO) has been a key task for satellites since the first time a satellite was put into space. The temporal and spatial resolution at which EO satellites take pictures has been increasing to support space-based applications, but this increases the amount of data each satellite generates. We observe that future EO satellites will generate so much data that this data cannot be transmitted to Earth due to the limited capacity of communication that exists between space and Earth. We show that conventional data reduction techniques such as compression [126] and early discard [41] do not solve this problem, nor does a direct enhancement of today's RF-based infrastructure [133], [153] for space-Earth communication. We explore an unorthodox solution instead - moving to space the computation that would have happened on the ground. This alleviates the need for data transfer to Earth. We analyze ten non-longitudinal RGB and hyperspectral image processing Earth observation applications for their computation and power requirements and discover that these requirements cannot be met by the small satellites that dominate today’s EO missions. We make a case for space micro-datacenters-large computational satellites whose primary task is to support in-space computation of EO data. We show that one 4KW space microdatacenter can support the computation need of a majority of applications, especially when used in conjunction with early discard. We do find, however, that communication between EO satellites and space microdatacenters becomes a bottleneck. We propose three space microdatacenter-communication co-design strategies – k–list-based network topology, microdatacenter splitting, and moving space microdatacenters to geostationary orbit–that alleviate the bottlenecks and enable effective usage of space microdatacenters.
NeuroLPM - Scaling Longest Prefix Match Hardware with Neural Networks

Longest Prefix Match engines (LPM) are broadly used in computer systems and especially in modern network devices such as Network Interface Cards (NICs), switches and routers. However, existing LPM hardware fails to scale to millions of rules required by modern systems, is often optimized for specific applications, and thus is performance-sensitive to the structure of LPM rules.We describe NeuroLPM, a new architecture for multi-purpose LPM hardware that replaces queries in traditional memory-intensive trie- and hash-table data structures with inference in a lightweight Neural Network-based model, called RQRMI. NeuroLPM scales to millions of rules under small on-die SRAM budget and achieves stable, rule-structure-agnostic performance, allowing its use in a variety of applications. We solve several unique challenges when implementing RQRMI inference in hardware, including minimizing the amount of floating point computations while maintaining query correctness, and scaling the rule-set size while ensuring small, deterministic off-chip memory bandwidth.We prototype NeuroLPM in Verilog and evaluate it on real-world packet forwarding rule-sets and network traces. NeuroLPM offers substantial scalability benefits without any application-specific optimizations. For example, it is the only algorithm that can serve a 950K-large rule-set at an average of 196M queries per second with 4.5MB of SRAM, only within 2% of the best-case throughput of the state-of-the-art Tree Bitmap and SAIL on smaller rule-sets. With 2MB of SRAM, it reduces the DRAM bandwidth per query, the dominant performance factor, by up to 9× and 3× compared to the state-of-the-art.CCS CONCEPTS• Hardware → Networking hardware; • Networks → Packet classification; • Computing methodologies → Machine learn ing.
ACRE: Accelerating Random Forests for Explainability

As machine learning models become more widespread, they are being increasingly applied in applications that heavily impact people’s lives (e.g., medical diagnoses, judicial system sentences, etc.). Several communities are thus calling for ML models to be not only accurate, but also explainable. To achieve this, recommendations must be augmented with explanations summarizing how each recommendation outcome is derived. Explainable Random Forest (XRF) models are popular choices in this space, as they are both very accurate and can be augmented with explainability functionality, allowing end-users to learn how and why a specific outcome was reached. However, the limitations of XRF models hamper their adoption, the foremost being the high computational demands associated with training such models to support high-accuracy classifications, while also annotating them with explainability meta-data.In response, we present ACRE, a hardware accelerator to support XRF model training. ACRE accelerates key operations that bottleneck performance, while maintaining meta-data critical to support explainability. It leverages a novel Processing-in-Memory hardware unit, co-located with banks of a 3D-stacked High-Bandwidth Memory (HBM). The unit locally accelerates the execution of key training computations, boosting effective data-transfer bandwidth. Our evaluation shows that, when ACRE augments HBM3 memory, it yields an average system-level training performance improvement of 26.6x, compared to a baseline multicore processor solution with DDR4 memory. Further, ACRE yields a 2.5x improvement when compared to an HBM3 architecture baseline, increasing to 5x when not bottlenecked by a 16k-thread limit in the host. Finally, due to much higher performance, we observe that ACRE provides a 16.5x energy reduction overall, over a DDR baseline.
MVC: Enabling Fully Coherent Multi-Data-Views through the Memory Hierarchy with Processing in Memory

Fusing computation and memory through Processing-in-Memory (PIM) provides a radical solution to the memory wall problem by minimizing communication overheads for data-intensive tasks, leading to a revolutionary shift in computer architecture. Although PIM has demonstrated promising results at different layers of the memory hierarchy, few studies have explored integrating compute memories into the memory management system, specifically in relation to coherence protocol. This paper presents MVC, a framework that leverages existing coherence protocols to enable fully coherent views throughout the memory hierarchy. By introducing coherent views, which are user-defined compact representations of conventional data structures, MVC can minimize data movement and harness the reusability of PIM output. The locality-aware MVC views significantly enhance the performance and energy efficiency of various irregular workloads.CCS CONCEPTS• Computer systems organization → Architectures; • Hardware → Memory and dense storage.
Impact of Voltage Scaling on Soft Errors Susceptibility of Multicore Server CPUs

Microprocessor power consumption and dependability are both crucial challenges that designers have to cope with due to shrinking feature sizes and increasing transistor counts in a single chip. These two challenges are mutually destructive: microprocessor reliability deteriorates at lower supply voltages that save power. An important dependability metric for microprocessors is their radiation-induced soft error rate (SER). This work goes beyond state-of-the-art by assessing the trade-offs between voltage scaling and soft error rate (SER) on a microprocessor system executing workloads on real hardware and a full software stack setup. We analyze data from accelerated neutron radiation testing for nominal and reduced microprocessor operating voltages. We perform our experiments on a 64-bit Armv8 multicore microprocessor built on 28 nm process technology. We show that the SER of SRAM arrays can increase up to 40.4% when the device operates at reduced supply voltage levels. To put our findings into context, we also estimate the radiation-induced Failures in Time (FIT) rate of various workloads for all the studied voltage levels. Our results show that the total and the Silent Data Corruptions (SDC) FIT of the microprocessor operating at voltage-scaled conditions can be 6.6× and 16× larger than at the nominal voltage, respectively. Moreover, changes in the microprocessor’s clock frequency do not have a noticeable impact on its soft error susceptibility. The findings of this work can aid computer architects in striking a balance between power and dependability, thus, designing more robust and efficient microprocessors.CCS CONCEPTS• Computer systems organization → Reliability; • Hardware → Transient errors and upsets; Power and energy.
Haemoglobin and C-reactive protein levels are efficient at identifying neoplasms as the most common early mortality of chronic diseases in emergency department patients

Introduction Extensive research has focused on emergency department (ED) and post-admission deaths, seeking to understand their frequency and causative factors. With the rising prevalence of advanced diseases, it is crucial to identify patients in need of end-of-life care and ensure its high quality. In this epidemiological study, we analyse routine ED blood tests to identify early warning signs of deteriorating patients with common non-traumatic and non-infectious (chronic) conditions. Material and methods We conducted a retrospective single-centre study for the years 2016–2019 using medical records and electronic data from the Multi-Specialistic Hospital in Gorzów Wielkopolski, Poland. We examined 8971 unique patients with circulatory, neoplastic, and endocrine diseases. We assessed the impact of 2 grouping variables (survivors and non-survivors) on a continuous outcome variable, including age and 37 routine blood tests. Results Two-way analysis of variance revealed that haemoglobin (Hb), haematocrit (Hct), and C-reactive protein (CRP) are the best differentiating biomarkers for early death in ED patients with cardiovascular, oncological, and endocrine diseases (excluding Hct due to its strong correlation with Hb). The Marczewski-Steinhaus taxonomy highlighted that oncological patients had the shortest survival time, averaging just 2 days from admission among ED non-survivors. Conclusions Among routinely tested ED biomarkers, Hb and CRP levels are efficient at identifying neoplasms as the most common early mortality of chronic diseases in ED patients.
Near field travel-time solutions for Anatolia

It is possible to use two methods in the near field travel-time solutions. These are single-station and two- station travel-time methods. Input data for these methods are travel times of many events at the single station and the differences of arrival-times at the couple of station lying on the same great circle with an event, respectively. In order to determine Pn-wave velocity and broad crustal structure beneath Anatolia, we have applied single-station method for various directions with an expanded data set, and composed the results with previous information obtained from both method. Data set relating to 84 events (mb≥4.6 and occurred since 1977) recorded at 22 stations have been used for computations. Pn-wave velocity has been determined as approximately 8.00 km/s. Thickness of crust is also 38 km as convenient with the previous investigations.