Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Crop Shortlisting Method for Sustainable Agricultural Diversification across Italy

Whilst current policies recognize the impacts that changes in climates and markets have imposed on the Italian food system, less attention is given to the development of systematic approaches to identify alternative cropping systems across Italy and the European continent. In this article, a novel evidence-based crop shortlisting method was developed to address crop diversification needs for Italy as an example for the whole of Europe. In order to shortlist possible options from a pool of 2700 crops, a crop–climate–soil matching exercise was performed across Italian territory, and crops with more than 70% suitability were chosen for further analysis. In the second phase, a multi-criteria ranking index was employed to assign ranks to chosen crops of four main types: (i) cereals and pseudocereals, (ii) legumes, (iii) starchy roots/tubers, and (iv) vegetables. To provide a comprehensive analysis, all of the abovementioned criteria were compared for both major crops that are grown in the region and potential underutilised crops (UCs). The results of evaluation of four major criteria (namely (a) calorie and nutrition demand, (b) functions and uses, (c) availability and accessibility to their genomic material, (d) possession of adaptive traits, and (e) physiological traits) revealed the potential for teff, faba bean, cowpea, green arrow arum, Jerusalem artichoke, Fig-leaved Gourd, and Watercress. We discuss the implication of utilising such systematic approaches to crop selection and developing transformative solutions for food security with the aim of providing a primer for mainstreaming UCs in policy and investment plans.
Pruned dynamic programming for optimal multiple change-point detection

A common computational problem in multiple change-point models is to recover the segmentations with $1$ to $K_{max}$ change-points of minimal cost with respect to some loss function. Here we present an algorithm to prune the set of candidate change-points which is based on a functional representation of the cost of segmentations. We study the worst case complexity of the algorithm when there is a unidimensional parameter per segment and demonstrate that it is at worst equivalent to the complexity of the segment neighbourhood algorithm: $\mathcal{O}(K_{max} n^2)$. For a particular loss function we demonstrate that pruning is on average efficient even if there are no change-points in the signal. Finally, we empirically study the performance of the algorithm in the case of the quadratic loss and show that it is faster than the segment neighbourhood algorithm.
Wireless Sensors Synchronization: An Accurate and Deterministic GPS-based Algorithm

Last decade, the need for wireless sensors solutions as core-solutions of Structural Monitoring gained in interest. The cost of wireless devices compared to the cost of wiring important structures (bridges, energy-plants, etc.) is attractive. Most of recent development in WSN domain focused on energy (saving or harvesting), on wireless proto-cols, on embedded algorithms. But it is a fact that, most of monitoring applications need samples to be time-stamped. According to the application, the wished time resolution could be up to one second for automation monitoring, one millisecond for vibration, one microsecond for acoustic monitoring, one nanosecond for electricity or light propagation. The consequence for a Wireless network of electronic nodes is that, by nature, no common signal could physically provide a synchronization top. But, as each electronic device, a wireless sensor time-base uses a timer incremented by a quartz whose initial value is theoretical up to some p.p.m. and whose period drift on time because of age, temperature, etc. Two kind of solutions could be regarded: a synchronization signal provided by the wireless protocol itself; an absolute synchronization from a referential source such as: GPS, Frankfurt clock, Galileo, etc. In the first way, it will be demonstrated the poor accuracy and the need of energy such a mechanism offers. In the second way, the article will details how a deterministic (Universal Time), accurate and resilient algorithm has been implemented. The article also provides specific results of application on acoustic monitoring system and electricity propagation where the accuracy of a WSN has reached up to 10 nanosecond UT. Consequence on energy consumption of this algorithm are given with a description of future works to improve the energy balance while keeping the device sober and synchronized.
Josephson Waveforms Characterization of a Sigma-Delta Analog-to-Digital Converter for Data Acquisition in Metrology

A sampling system based on a 24-bits sigma-delta analog-to-digital converter (ADC) was built and characterized in order to study the feasibility of using this type of ADCs in electrical metrology. The non-linearities of the sampling system have been studied and a model for postcorrecting the measured data points established. The Hammerstein model, consisting of a static non-linear part and a linear system, was employed. A 4-th order polynomial accounts for the non-linearities of the analog electronics and the input stages of the sigma delta ADC. The linear part corresponds to the transfer function of the decimation filters internal to the ADC. The parameters for the model of the system were determined using noiseless and drift-free waveforms from a Josephson waveform synthesizer. The performance of the sampling system was verified experimentally by comparing the measured root-mean-square (rms) value of sinusoidal signals with the results from an established method. The results obtained using the post-corrected samples from the sampling system at 125 Hz agreed to within 2 μV/V with the de facto standard in metrology laboratories, which uses a high accuracy digital voltmeter. Precision measurements are limited by the decimation filters inside the ADC and can only be carried out for frequencies below 1/24-th of the equivalent sampling rate. The characterization results have shown that the non-linearities have been compensated to 5 μV/V or better and the effective resolution exceeds 20 bits, over an input range of 1 V at the equivalent sampling rate of 32 kHz. The experimental validation has proved that it is possible to measure rms values of sinusoidal signals with 1 V peak amplitudes for frequencies up to 1.3 kHz with uncertainty of 8 μV/V, significantly improving the uncertainty achievable with de facto standard which reaches 8 μV/V at 500 Hz.
Authentication at the Physical Layer with Cooperative Communications and Machine Learning

Physical layer authentication allows users to be authenticated only on the basis of the peculiar characteristics of their transmission channels. With the aim of improving physical layer authentication accuracy, we consider cooperative communications exploiting a set of relay nodes located between the supplicant and the authenticator. These relay nodes can behave in different ways, passively or actively cooperating to establish whether or not to forward messages to the final recipient. The presence of cooperating nodes provides the authenticator with a large number of features, which can be exploited and combined to identify the original source of packets using both statistical and machine learning-based techniques. We also consider two basic power allocation strategies, and provide performance assessments based on both theoretical arguments and numerical simulations.
Target Specific Uptake of a Newly Synthesized Radiolabeled 5α-Reductase Inhibitor “Tc-99m-17-Oxo-17a-Aza-D-Homo-5-Androsten-3β-yl Phenoxyacetate (Tc-99m-17a-Aza Steroid)” in Rat Prostatic Neoplastic Lesions

Objective: Considering the 5α-reductase (5AR) inhibitory activity of the oximes and the importance of the ester group in increasing the anti-androgenic property, we reasoned to synthesize a compound having a lactam group in ring D and an ester group at the 3 β position of the androsterone nucleus. The study aims to radiolabel 17-oxo-17a-aza-D-homo-5-androsten-3β-yl phenoxyacetate (17a-aza steroid) with Tc-99m to evaluate its targeted uptake in experimentally induced prostate carcinogenesis in rats. Materials and Methods: The prediction of the optimal interaction and binding affinity of Tc-99m-17-oxo-17a-aza-D-homo-5-androsten-3 β-ylphenoxyacetate (Tc-99m-17a-aza steroid) toward 5AR inhibitor was done using Biopredicta Vlife MDS tool. Tc-99m-17a-aza steroid was developed by direct radiolabeling protocol. The radio-pharmacological characteristics (serum stability, plasma protein-binding ability, and lipophilicity) of the complex were evaluated. Further, the bio-distribution studies of the complex were performed in rats with experimentally induced prostate carcinogenesis. Results: The in-silico analysis exhibits favorable binding of Tc-99m-17a-aza toward 5AR with D score-130.97. The radiochemical purity of Tc-99m-17a-aza was 96.79%. The radio-complex maintained stability in the rat serum for a period of 6 h (hours). Plasma protein binding and Log Po/w value were observed to be 86.23 ± 7.08% and 0.118 ± 0.045, respectively. A significantly enhanced percent-specific uptake was observed in the prostate of rats with induced prostate carcinogenesis. Conclusion: The study concludes that Tc-99m-17a-aza exhibits prostate specificity and can be explored further for its potential as a radionuclide imaging probe.
Identification of a Novel Risk Model: A Five-Gene Prognostic Signature for Pancreatic Cancer

Objective Biomarkers for pancreatic cancer (PCa) prognosis provide evidence for improving the survival outcome of this disease. This study aimed to identify a prognostic risk model based on gene expression profiling of microarray bioinformatics analysis. Methods Prognostic immune genes in the TCGA-PAAD cohort were identified using the univariate Cox regression and Kaplan–Meier survival analysis. Multivariate Cox regression (stepAIC) was used to identify prognostic genes from the top 20 hub genes in the protein-protein interaction (PPI) network. A prognostic risk model was established and its performance in predicting the overall survival in PCa was validated in GSE62452. Gene mutations and infiltration immune cells in PCa tumors were analyzed using online databases. Results Univariate Cox regression and Kaplan–Meier survival analyses identified 128 prognostic genes. Multivariate Cox regression (stepAIC) identified five prognostic genes (PLCG1, MET, TNFSF10, CXCL9, and TLR3) out of the 20 hub genes in the PPI network. A prognostic risk model was established using the signature of five genes. This model had moderate to high accuracies (AUC > 0.700) in predicting 3-year and 5-year overall survival in TCGA and GSE62452 cohorts. The Kaplan–Meier survival analysis showed that high-risk scores were correlated with poor survival outcomes in PCa (p < 0.05). Also, mutations in the five genes were related to poor survival. The five genes were related to multiple immune cells. Conclusions The prognostic risk model was significantly correlated with the survival in PCa patients. This model modulated PCa tumor progression and prognosis by regulating immune cell infiltration.
Visually Identified Tau 18F-MK6240 PET Patterns in Symptomatic Alzheimer’s Disease

Background: In Alzheimer’s disease, heterogeneity has been observed in the postmortem distribution of tau neurofibrillary tangles. Visualizing the topography of tau in vivo may facilitate clinical trials and clinical practice. Objective: This study aimed to investigate whether tau distribution patterns that are limited to mesial temporal lobe (MTL)/limbic regions, and those that spare MTL regions, can be visually identified using 18F-MK6240, and whether these patterns are associated with different demographic and cognitive profiles. Methods: Tau 18F-MK6240 PET images of 151 amyloid-β positive participants with mild cognitive impairment (MCI) and dementia were visually rated as: tau negative, limbic predominant (LP), MTL-sparing, and Typical by two readers. Groups were evaluated for differences in age, APOE ɛ4 carriage, hippocampal volumes, and cognition (MMSE, composite memory and non-memory scores). Voxel-wise contrasts were also performed. Results: Visual rating resulted in 59.6% classified as Typical, 17.9% as MTL-sparing, 9.9% LP, and 12.6% as tau negative. Intra-rater and inter-rater reliability was strong (Cohen’s kappa values of 0.89 and 0.86 respectively). Tracer retention in a “hook”-like distribution on sagittal sequences was observed in the LP and Typical groups. The visually classified MTL-sparing group had lower APOE ɛ4 carriage and relatively preserved hippocampal volumes. Higher MTL tau was associated with greater amnestic cognitive impairment. High cortical tau was associated with greater impairments on non-memory domains of cognition, and individuals with high cortical tau were more likely to have dementia than MCI. Conclusion: Tau distribution patterns can be visually identified using 18F-MK6240 PET and are associated with differences in APOE ɛ4 carriage, hippocampal volumes, and cognition.
A Novel Threshold Voltage Defined Multiplexer for Interconnect Camouflaging

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Ultrasonic Detection of High-Intensity Focused Ultrasound Field using Quadrature Point Phase Step in a Fiber Optic Interferometric Sensor

A compact low-temperature crosstalk all-fiber ultrasonic sensor based on quadrature point phase step is proposed and experimentally demonstrated for the detection of high-intensity focused ultrasound (HIFU). The end facet of the graded index fiber was microfabricated with a 3π/2 phase step, which forms an ultra-compact two-wave interferometer. The ultrasonic wave front can be simply detected with phase-front modulation. The all-fiber configuration allows the sensor to detect the HIFU field at a high intensity level. More importantly, the proposed sensor is virtually temperature independent due to the ultra-thin phase step. The experimental results show that the ultrasonic sensor has a sensitivity of 324 mV/MPa, and the standard variation of the temperature cross sensitivity is only 1.7%. The proposed fiber sensor is expected to have potential applications in HIFU detection due to its tolerance at high intensity levels and low-temperature cross sensitivity.
Impact of Machine Perfusion on the Immune Response After Liver Transplantation – A Primary Treatment or Just a Delivery Tool

The frequent use of marginal livers forces transplant centres to explore novel technologies to improve organ quality and outcomes after implantation. Organ perfusion techniques are therefore frequently discussed with an ever-increasing number of experimental and clinical studies. Two main approaches, hypothermic and normothermic perfusion, are the leading strategies to be introduced in clinical practice in many western countries today. Despite this success, the number of studies, which provide robust data on the underlying mechanisms of protection conveyed through this technology remains scarce, particularly in context of different stages of ischemia-reperfusion-injury (IRI). Prior to a successful clinical implementation of machine perfusion, the concept of IRI and potential key molecules, which should be addressed to reduce IRI-associated inflammation, requires a better exploration. During ischemia, Krebs cycle metabolites, including succinate play a crucial role with their direct impact on the production of reactive oxygen species (ROS) at mitochondrial complex I upon reperfusion. Such features are even more pronounced under normothermic conditions and lead to even higher levels of downstream inflammation. The direct consequence appears with an activation of the innate immune system. The number of articles, which focus on the impact of machine perfusion with and without the use of specific perfusate additives to modulate the inflammatory cascade after transplantation is very small. This review describes first, the subcellular processes found in mitochondria, which instigate the IRI cascade together with proinflammatory downstream effects and their link to the innate immune system. Next, the impact of currently established machine perfusion strategies is described with a focus on protective mechanisms known for the different perfusion approaches. Finally, the role of such dynamic preservation techniques to deliver specific agents, which appear currently of interest to modulate this posttransplant inflammation, is discussed together with future aspects in this field.
Chaos assisted variable bit steganography in transform domain

An efficient variable bit data hiding scheme based on combined chaotic system (CCS) and integer wavelet transform (IWT) is proposed. With the aim to maximise the security level, CCS is developed by combining two 1D chaotic maps. The developed combined tent-logistic (CTL), combined sine-logistic (CSL) and combined sine-tent (CST) maps with improved chaotic behaviour are utilised to generate the key sequences. These chaotic key sequences from CTL, CSL and CST are then quantised to embed the secret bits in high-high, high-low and low-high sub-bands of the IWT transformed cover image, respectively. The main advantage of this scheme is that the number of bits to be embedded in each and every sub-band coefficients is highly chaotic and extremely sensitive to the initial seeds.
Agronomic traits and seed yield produced in the soybean-corn crop in succession cropping - doi: 10.4025/actasciagron.v32i4.8064

Competition experiments were installed at the COODETEC unit in Palotina, Parana State, with the objective of evaluating the influence of the cultivation of soybean in succession to a second crop season on different sowing dates on the agronomic traits and seed yield in both cultures. The experiment consisted of four field replicates, arranged in a completely randomized block design and carried out in two agricultural years (2003/2004 and 2004/2005). The individual blocks were comprised of three early-maturing soybean cultivars (CD 202, CD 215 and CD 216) sowed on five different dates (9/15, 9/30, 10/15, 10/30 and 11/15) and also three corn hybrids (CD 305, CD 306 and CD 308) that were sowed on different dates (1/30, 2/15, 3/1, 3/15 and 3/30). Data were submitted to a joint analysis, and the interaction decompositions were made when verified significance. Based on the results obtained, it can be concluded that early season second crop growing is a valid strategy for obtaining a good agronomical performance and better productivity in off-season corn sowing. The succession of early soybean/early corn in the off-season is a potentially viable alternative for the west region of Parana State, as long as the soybean sowing is done by the first half of October and the second crop season corn is sown in the first half of February.
Risk preference evaluation – a fourth dimension of the application of the Laplace transform

In this paper, we apply the Laplace transform to risk preference theory (decision theory, decision analysis). We show that with a constant measure of absolute risk aversion, the certainty monetary equivalent (CME) can be developed into an expression involving the logarithm of the bilateral Laplace transform of the probability density of the outcome of risky projects. We also introduce a measure named internal risk aversion (IRA). This is in analogy with the earlier application of the transform to net present value problems. Properties of the CME function are examined. Basic examples of application are introduced followed by applications to portfolio theory and option pricing.
The Use of Polymer Blends in the Treatment of Ocular Diseases

The eye is an organ with limited drug access due to its anatomical and physiological barriers, and the usual forms of ocular administration are limited in terms of drug penetration, residence time, and bioavailability, as well as low patient compliance. Hence, therapeutic innovations in new drug delivery systems (DDS) have been widely explored since they show numerous advantages over conventional methods, besides delivering the content to the eye without interfering with its normal functioning. Polymers are usually used in DDS and many of them are applicable to ophthalmic use, especially biodegradable ones. Even so, it can be a hard task to find a singular polymer with all the desirable properties to deliver the best performance, and combining two or more polymers in a blend has proven to be more convenient, efficient, and cost-effective. This review was carried out to assess the use of polymer blends as DDS. The search conducted in the databases of Pubmed and Scopus for specific terms revealed that although the physical combination of polymers is largely applied, the term polymer blend still has low compliance.
Single-Cell Patterning Based on Immunocapture and a Surface Modified Substrate

: Micropatterning technology offers powerful methods for biological analyses at the molecular level, enabling the investigation of cell heterogeneities, as well as high throughput detection. We herein propose an approach for single-cell patterning. The substrate was prepared using micro fabrication and surface modiﬁcation processes, and the patterning template was prepared using bovine serum albumin and streptavidin, which can be employed for the patterning of any biological molecules containing biotin. Subsequent to photolithography, etching, chemical vapor deposition (CVD), and polyethylene glycol (PEG) treatment, the optimized patterns were obtained with high accuracy, strong contrast, and good repeatability, thus providing good foundations for the subsequent single-cell patterning. The surface passivation method was proven effective, preventing unwanted binding of the antibodies and cells. Based on this streptavidin template, the speciﬁc binding between the biotinylated antibodies and the antigens expressed on the surface of the cells was enabled, and we successfully achieved single-cell patterning with a single-cell capture rate of 92%. This single-cell array offers an effective method in the investigation of cell heterogeneity and drug screening. Further, these methods can be used in the ﬁnal step for the screening and enrichment of certain cells, such as circulating tumor cells.