Miracle-dz/UniFAR-FTUs huggingface dataset:
Static, cyclic and post liquefaction undrained behaviour of Fraser river sand

An experimental study of static, cyclic and post cyclic undrained behaviour of Fraser river sand in the triaxial test is presented. Static undrained behaviour over a range of deposition densities, from loosest to dense, and a range of confining stress is studied. Compression as well as extension loading paths are included. It is shown that for identical density and stress state the sand is dilative in triaxial compression but contractive in triaxial extension loading. It is shown that at a given confining stress static shear stress can change a dilative sand into a contractive one. Cyclic loading leading to liquefaction is studied at specific targeted densities from loose to dense at a range of confining stress and the liquefaction resistance determined. It is shown that the sand can undergo large deformation even before a state of 100% pore pressure ratio occurs. There is no effect of the level of confining stress on liquefaction resistance of loose sand. The resistance of denser sand, however, decreases with increase in confining stress. During post liquefaction undrained monotonie loading, the sand initially deforms with an essentially zero stiffness which then increases with the level of strain.The effect of maximum shear strain due to cyclic loading, relative density, mode of loading and the level of confining stress prior to cyclic loading on the post liquefaction monotonie undrained response of the sand is investigated. Volumetric strain due to dissipation of excess pore pressure until liquefaction is assessed as a function of maximum shear strain, relative density and confining stress prior to inducing liquefaction. The similarity of post liquefaction behaviour (both stress-strain response and volumetric strain) between sand brought to the liquefied state by cyclic loading and by static load/unload cycle is also investigated.
ACE I/D genotype associates with strength in sarcopenic men but not with response to ACE inhibitor therapy in older adults with sarcopenia: Results from the LACE trial

Background Angiotensin II (AII), has been suggested to promote muscle loss. Reducing AII synthesis, by inhibiting angiotensin converting enzyme (ACE) activity has been proposed as a method to inhibit muscle loss. The LACE clinical trial was designed to determine whether ACE inhibition would reduce further muscle loss in individuals with sarcopenia but suffered from low recruitment and returned a negative result. Polymorphic variation in the ACE promoter (I/D alleles) has been associated with differences in ACE activity and muscle physiology in a range of clinical conditions. This aim of this analysis was to determine whether I/D polymorphic variation is associated with muscle mass, strength, in sarcopenia or contributed to the lack of response to treatment in the LACE study. Methods Sarcopenic individuals were recruited into a 2x2 factorial multicentre double-blind study of the effects of perindopril and/or leucine versus placebo on physical performance and muscle mass. DNA extracted from blood samples (n = 130 72 women and 58 men) was genotyped by PCR for the ACE I/D polymorphism. Genotypes were then compared with body composition measured by DXA, hand grip and quadriceps strength before and after 12 months’ treatment with leucine and/or perindopril in a cross-sectional analysis of the influence of genotype on these variables. Results Allele frequencies for the normal UK population were extracted from 13 previous studies (I = 0.473, D = 0.527). In the LACE cohort the D allele was over-represented (I = 0.412, D = 0.588, p = 0.046). This over-representation was present in men (I = 0.353, D = 0.647, p = 0.010) but not women (I = 0.458, D = 0.532, p = 0.708). In men but not women, individuals with the I allele had greater leg strength (II/ID = 18.00 kg (14.50, 21.60) vs DD = 13.20 kg (10.50, 15.90), p = 0.028). Over the 12 months individuals with the DD genotype increased in quadriceps strength but those with the II or ID genotype did not. Perindopril did not increase muscle strength or mass in any polymorphism group relative to placebo. Conclusion Our results suggest that although ACE genotype was not associated with response to ACE inhibitor therapy in the LACE trial population, sarcopenic men with the ACE DD genotype may be weaker than those with the ACE I/D or II genotype.
High Rates of Seroprotection and Seroconversion to Vaccine-Preventable Infections in the Early Post–Autologous Stem Cell Transplant Period

Abstract In patients early post–autologous stem cell transplant, seroprotection rates were high for Hemophilus influenzae type B and tetanus toxoid (70%–90%) but lower for Streptococcus pneumoniae (30%–50%) including after revaccination. There were high rates of seropositivity (67%–86%) to measles, mumps, and rubella and varicella zoster virus. Durability of protection requires assessment.
Latitudinal and meridional patterns of picophytoplankton variability are contrastingly associated with Ekman pumping and the warm pool in the tropical western Pacific

Abstract Marine picophytoplankton plays a major role in marine cycling and energy conversion, and its effects on the carbon cycle and global climate change have been well documented. In this study, we investigated the response of picophytoplankton across a broad range of physicochemical conditions in two distinct regions of the tropical western Pacific. Our analysis considered the abundance, carbon biomass, size fraction, distribution, and regulatory factors of the picophytoplankton community, which included the cyanobacteria Prochlorococcus and Synechococcus, and small eukaryotic phytoplankton (picoeukaryotes). The first region was a latitudinal transect along the equator (142–163° E, 0° N), characterized by stratified oligotrophic conditions. The second region was a meridional transect (143° E, 0–22° N) known for its high‐nutrient and low‐chlorophyll (HNLC) conditions. Results showed that picophytoplankton contributed >80% of the chlorophyll a (Chl a), and was mainly distributed above 100 m. Prochlorococcus was the dominant organism in terms of cell abundance and estimated carbon biomass in both latitudinal and meridional transects, followed by Synechococcus and picoeukaryotes. In the warm pool, Prochlorococcus was primarily distributed below the isothermal layer, with the maximum subsurface abundance forming below it. The maximum Synechococcus abundance was restricted to the west‐warm pool, due to the high temperature, and the second‐highest Synechococcus abundance was associated with frontal interaction between the east‐warm pool and the westward advance of Middle East Pacific water. In contrast, picoeukaryotes formed a maximum subsurface abundance corresponding to the subsurface Chl a maximum. In the mixed HNLC waters, the cell abundance and biomass of the three picophytoplankton groups were slightly lower than those in the warm pool. Due to a cyclonic eddy, the contours of the maximum subsurface Prochlorococcus abundance were uplifted, evidently with a lower value than the surrounding water. Synechococcus abundance varied greatly in patches, forming a weakly high subsurface peak when the isothermal layer rose to the near‐surface (<50 m). The subsurface maximum picoeukaryote abundance was also highly consistent with that of the subsurface Chl a maximum. Correlation analysis and generalized additive models of environmental factors showed that nutrient availability had a two‐faceted role in regulating the spatial patterns of picophytoplankton in diverse latitudinal and meridional environments. We concluded through regression that temperature and light irradiance were the key determinants of picophytoplankton variability in the tropical western Pacific. This study provides insights into the changing picophytoplankton community structure with potential future changing hydroclimatic force.
Assessing the Impact of Primary Care-Led Optimisation Clinics in the Management of Type 2 Diabetes: A Service Redesign Pilot Study

Background Many individuals diagnosed with type 2 diabetes (T2D) in the London Borough of Waltham Forest, treated solely with oral hypoglycaemic medications (OHAs), exhibit increased levels of glycated haemoglobin (HbA1c). While specialised community and secondary care clinics are at full capacity, a gap exists for dedicated diabetes optimisation services at the primary care level. This study aimed to launch a remote Primary Care Network (PCN)-based clinic during the coronavirus disease 2019 (COVID-19) pandemic to enhance the management of OHAs, introduce motivational interviewing, and incorporate patient empowerment strategies in tandem with a secondary care endocrinologist. The primary objective was to evaluate the impact on HbA1c levels and other metabolic parameters. Concurrently, the “behaviour change model” served to measure patient engagement. Methodology We recruited 43 patients in this study, each undergoing a 30-minute consultation focused on diabetes management. A dedicated administrator ensured patient engagement and a three-month follow-up with repeat metabolic profile testing. Sustained, high-quality care was upheld through bimonthly remote consultations, receiving expertise from an endocrinology consultant. Results Of the pilot’s 38 patients managed solely with OHAs, 31 achieved an HbA1c reduction of more than 11 mmol/mol. The overall median reduction for the entire cohort was significant (initially 88 mmol/mol versus 70 mmol/mol, p < 0.0001). Triglyceride levels also saw a notable median decline (1.56 mmol/L down to 1.20 mmol/L, p = 0.0247). Of the 38 completing the pilot, 14 had behavioural stages recorded both initially and at follow-up. Employing motivational interviewing led to significant diabetes-related behavioural changes in 11 of the 14 patients. Conclusions A PCN-based optimisation clinic, augmented with active recall strategies, was a cost-effective method to boost awareness, self-management, and glycaemic control among individuals with T2D. These PCN-led clinics orchestrated by primary care clinicians offer a streamlined solution for achieving treatment benchmarks even amid the challenges of the COVID-19 pandemic.
How much do field mice prefer dwarf bamboo seeds? Two‐choice experiments between seeds of Sasa borealis and several tree species on the forest floor

Abstract Bambusoideae is a taxon of mass‐flowering monocarpic perennials with a long life cycle. Forest ecosystems are affected by Bambusoideae seeding and death events in various ways, including an increased abundance of Apodemus spp. The utilization and preference of dwarf bamboo seeds over tree seeds by field mice remain elusive. Therefore, we aimed to determine whether field mice prefer dwarf bamboo to tree seeds. We examined one dwarf bamboo species (Sasa borealis) against four tree species with varying acorn/fruit traits (Castanea crenata, Quercus crispula, Fagus crenata, and Lindera triloba). The seeds were placed in a container in a forest among dead S. borealis culms, with an automatic camera monitoring the setup. The examined seeds were mainly foraged by two field mouse species, Apodemus speciosus and Apodemus argenteus, with preference in the following order: C. crenata, L. triloba, S. borealis, F. crenata, and Q. crispula. Our findings indicated that during S. borealis mast seeding years, predation pressure on F. crenata and Q. crispula seeds could be considerably reduced. This suggests that mast seeding might disrupt the normal pattern of survival, and seed dispersal patterns, potentially altering the forest vegetation composition.
Reassessing Wind Potential Estimates for India: Economic and Policy Implications

E RNEST O RLANDO L AWRENCE B ERKELEY N ATIONAL L ABORATORY Environmental Energy Technologies Division Reassessing Wind Potential Estimates for India: Economic and Policy Implications Amol Phadke Ranjit Bharvirkar Jagmeet Khangura July 2011
Slow-light silicon modulator with 110-GHz bandwidth

Silicon modulators are key components to support the dense integration of electro-optic functional elements for various applications. Despite numerous advances in promoting the modulation speed, a bandwidth ceiling emerges in practices and becomes an obstacle toward Tbps-level throughput on a single chip. Here, we demonstrate a compact pure silicon modulator that shatters present bandwidth ceiling to 110 gigahertz. The proposed modulator is built on a cascade corrugated waveguide architecture, which gives rise to a slow-light effect. By comprehensively balancing a series of merits, the modulators can benefit from the slow light for better efficiency and compact size while remaining sufficiently high bandwidth. Consequently, we realize a 110-gigahertz modulator with 124-micrometer length, enabling 112 gigabits per second on-off keying operation. Our work proves that silicon modulators with 110 gigahertz are feasible, thus shedding light on its potentials in ultrahigh bandwidth applications such as optical interconnection and photonic machine learning.
Time series forecasting of COVID-19 infections and deaths in Alpha and Delta variants using LSTM networks

Since the beginning of the rapidly spreading COVID-19 pandemic, several mutations have occurred in the genetic sequence of the virus, resulting in emerging different variants of concern. These variants vary in transmissibility, severity of infections, and mortality rate. Designing models that are capable of predicting the future behavior of these variants in the societies can help decision makers and the healthcare system to design efficient health policies, and to be prepared with the sufficient medical devices and an adequate number of personnel to fight against this virus and the similar ones. Among variants of COVID-19, Alpha and Delta variants differ noticeably in the virus structures. In this paper, we study these variants in the geographical regions with different size, population densities, and social life styles. These regions include the country of Iran, the continent of Asia, and the whole world. We propose four deep learning models based on Long Short-Term Memory (LSTM), and examine their predictive power in forecasting the number of infections and deaths for the next three, next five, and next seven days in each variant. These models include Encoder Decoder LSTM (ED-LSTM), Bidirectional LSTM (Bi-LSTM), Convolutional LSTM (Conv-LSTM), and Gated Recurrent Unit (GRU). Performance of these models in predictions are evaluated using the root mean square error, mean absolute error, and mean absolute percentage error. Then, the Friedman test is applied to find the leading model for predictions in all conditions. The results show that ED-LSTM is generally the leading model for predicting the number of infections and deaths for both variants of Alpha and Delta, with the ability to forecast long time intervals ahead.
Generative models for protein sequence modeling: recent advances and future directions

Abstract The widespread adoption of high-throughput omics technologies has exponentially increased the amount of protein sequence data involved in many salient disease pathways and their respective therapeutics and diagnostics. Despite the availability of large-scale sequence data, the lack of experimental fitness annotations underpins the need for self-supervised and unsupervised machine learning (ML) methods. These techniques leverage the meaningful features encoded in abundant unlabeled sequences to accomplish complex protein engineering tasks. Proficiency in the rapidly evolving fields of protein engineering and generative AI is required to realize the full potential of ML models as a tool for protein fitness landscape navigation. Here, we support this work by (i) providing an overview of the architecture and mathematical details of the most successful ML models applicable to sequence data (e.g. variational autoencoders, autoregressive models, generative adversarial neural networks, and diffusion models), (ii) guiding how to effectively implement these models on protein sequence data to predict fitness or generate high-fitness sequences and (iii) highlighting several successful studies that implement these techniques in protein engineering (from paratope regions and subcellular localization prediction to high-fitness sequences and protein design rules generation). By providing a comprehensive survey of model details, novel architecture developments, comparisons of model applications, and current challenges, this study intends to provide structured guidance and robust framework for delivering a prospective outlook in the ML-driven protein engineering field.
HOMA-IR is an effective biomarker of non-alcoholic fatty liver disease in non-diabetic population

Objectives This study aimed to investigate the correlation between homeostasis model assessment of insulin resistance (HOMA-IR) and non-alcoholic fatty liver disease (NAFLD) in the non-diabetic population and establish its diagnostic efficacy. Methods This observational study involved participants divided into NAFLD and non-NAFLD groups, and baseline data were analyzed. Univariate and multivariate logistic regression analyses were used to correlate HOMA-IR with the risk of NAFLD. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy of HOMA-IR for NAFLD. Subgroup analyses of non-obese individuals were performed. Results Overall, 2234 non-diabetic participants were included. The HOMA-IR was significantly higher in the NAFLD group than in the non-NAFLD group. Multivariate logistic regression analysis showed that HOMA-IR was a strong and independent risk factor for NAFLD after correcting for confounding factors. The area under the ROC curve (AUC) value of HOMA-IR for predicting NAFLD was 0.792. In the non-obese non-diabetic population, HOMA-IR was an independent risk factor for increased risk of lean NAFLD after correcting for confounding factors. The AUC value of HOMA-IR for predicting lean NAFLD was 0.770. Conclusions HOMA-IR is independently associated with the risk of NAFLD in the non-diabetic and non-obese non-diabetic populations and has good diagnostic value.
Differential effects of conventional and high‐definition transcranial direct‐current stimulation of the motor cortex on implicit motor sequence learning

Conventional transcranial direct‐current stimulation (tDCS) delivered to the primary motor cortex (M1) has been shown to enhance implicit motor sequence learning (IMSL). Conventional tDCS targets M1 but also the motor association cortices (MAC), making the precise contribution of these areas to IMSL presently unclear. We aimed to address this issue by comparing conventional tDCS of M1 and MAC to 4 * 1 high‐definition (HD) tDCS, which more focally targets M1. In this mixed‐factorial, sham‐controlled, crossover study in 89 healthy young adults, we used mixed‐effects models to analyse sequence‐specific and general learning effects in the acquisition and short‐ and long‐term consolidation phases of IMSL, as measured by the serial reaction time task. Conventional tDCS did not influence general learning, improved sequence‐specific learning during acquisition (anodal: M = 42.64 ms, sham: M = 32.87 ms, p = .041), and seemingly deteriorated it at long‐term consolidation (anodal: M = 75.37 ms, sham: M = 86.63 ms, p = .019). HD tDCS did not influence general learning, slowed performance specifically in sequential blocks across all learning phases (all p's < .050), and consequently deteriorated sequence‐specific learning during acquisition (anodal: M = 24.13 ms, sham: M = 35.67 ms, p = .014) and long‐term consolidation (anodal: M = 60.03 ms, sham: M = 75.01 ms, p = .002). Our findings indicate that the observed superior conventional tDCS effects on IMSL are possibly attributable to a generalized stimulation of M1 and/or adjacent MAC, rather than M1 alone. Alternatively, the differential effects can be attributed to cathodal inhibition of other cortical areas involved in IMSL by the 4 * 1 HD tDCS return electrodes, and/or more variable electric field strengths induced by HD tDCS, compared with conventional tDCS.
COVID-19 Infections and Inflammatory Markers in Patients Hospitalized During the First Year of the Pandemic

Background: The clinical presentations in patients with SARS-CoV-2 (COVID-19) infections range from asymptomatic upper respiratory infections to acute respiratory failure with bilateral pulmonary infiltrates requiring mechanical ventilation. Clinicians often measured inflammatory markers in hospitalized patients to characterize the severity of the infection. Multiple studies published in 2020 reported information on the frequency of elevated inflammatory markers in hospitalized patients in various categories of disease severity. Methods: The PubMed database was searched using the terms “Inflammatory markers in COVID-19 patients” and “Clinical features of patients infected with COVID-19. ” Thirty-three publications were analyzed in detail to determine which inflammatory markers were increased and the frequency of these increases in various clinical classifications, including patients requiring hospitalization, patients with pneumonia, patients with severe infection, patients requiring intensive care unit admission, and patients who died. Results: C-reactive protein (CRP) was the most frequently elevated inflammatory marker in all categories. Ferritin, D-dimer, and erythrocyte sedimentation rate levels were also frequently elevated. In general, frequencies were higher in patients with more severe infections. For example, 24 out of 24 patients who died had an elevated CRP level. Conclusion: This review provides concrete information about the frequency of various inflammatory markers in patients with COVID-19 infection who required hospitalization. It also provides us some insight into the approach clinicians took in the early phase of this pandemic in their efforts to characterize these patients and perhaps understand the disease process better. However, these results might suggest that clinicians and laboratory directors should develop protocols to optimize laboratory testing.
Family History and Risk of Renal Cell Carcinoma: A National Multiregister Case-Control Study

Purpose: Our purpose was to investigate the association between family history of renal cell carcinoma (RCC) and RCC risk. Materials and Methods: RCC cases diagnosed in Sweden between 2005 and 2014 and 10 matched controls were identified using the Renal Cell Cancer Database Sweden, with linkage to the Multigeneration Register and the Swedish Cancer Registry. The association between a family history of RCC and RCC was investigated, overall and by sex and age groups. Results: Among 9416 RCC cases, 294 (3.1%) had 1 or more parent or sibling (first-degree relative [FDR]) with RCC. Median age at diagnosis for cases with an affected FDR was 65 years (IQR 59-71) and 68 years (IQR 60-75) for all cases. The proportion of women was significantly higher among familial RCC compared to sporadic RCC (44.6% vs 38.5%, P = .035). RCC was twice as likely with 1 or more FDR with RCC (OR 1.9; CI 1.65-2.16). Stratified analysis showed an OR of 2.4 for women (CI 1.93-2.92) and 1.6 for men (CI 1.35-1.93). Two or more FDRs was associated with a sixfold increased risk (95% CI 2.37-15.5). Familial RCC was strongly associated with bilateral and multifocal tumors (OR 5.5; CI 2.36-13.0, OR 3.5; CI 1.89-6.49). Conclusions: In this Swedish data set, 3.1% of RCC patients have 1 or more FDR diagnosed with RCC. There was no statistical difference in median age between sporadic RCC and familial RCC. Having 1 or more FDR with RCC approximately doubles the risk of RCC with a higher risk increase for women than for men. People with 2 FDRs with RCC constitute a small high-risk group that may benefit from screening.