Miracle-dz/UniFAR-FTUs huggingface dataset:
Determinants of Participation in Contract Farming Among Small Holder Diary Farmers in Rwanda

Milk production is one of the major income generating activities for smallholder dairy farmers in Rwanda but the sector is currently facing production and marketing challenges. Contract farming has been introduced as solution. However, low participation has hindered dairy farmers from getting optimum benefits accruing from contract farming not only low participation but the determinants of participation in contracting are still under research. The aim of this study was to analyze the determinants of participation in contract farming to the smallholder dairy farmers in Nyagatare District, Eastern Province of Rwanda. A multi stage sampling techniques were employed to select respondents. Structure questionnaires were employed to collect data from 211 . Probit model was used to determine factors influencing farmers ‘participation, results from the probit model suggests that factors influencing farmers participation mainly include; uncertainty related to price, farm size, herd size of cross breeds, distance to milk collection centers, distance to water source, experience in dairy farming and trainings in forage management. The study concludes that in order to increase participation in contract farming, there is need for diary policy to consider existing knowledge, assets of the farmers and decentralize milk Collection Centers.
Cement Stabilization of Highly Organic Subgrade Soils to Control Secondary Compression Settlement

Many flexible pavements in western Palm Beach County, Florida, are underlain at shallow depths by thick deposits of organic soils and peats. These soils undergo long-term secondary compression as a result of sustained overburden pressure of the pavement and thereby cause excessive premature structural distress in the form of cracking, rutting, and differential settlement. Although cementitious materials have been successfully used to stabilize soft, expansive, and inorganic soils, research and experience regarding the stabilization of highly organic soils are limited. The authors’ main motivation for doing this research was to investigate the effects of cement stabilization on the compressibility of soils consisting of organic content in the range of 67% to 90%. The undisturbed soil samples were collected from the subsurface of the SR-15 (US-98) roadway, which had experienced severe distress attributable to organic layers and had undergone frequent and costly rehabilitation in recent years. It was found that cement stabilization at dosages between 35% and 55% (by dry weight) drastically reduced the ratio of the secondary compression index to the primary compression index of the organic soils to values that resembled a nearly granular soil with desirable compressibility characteristics. This optimized mix design may provide appropriate guidelines for deep mixing methods in subsurface organic layers for the long-term preservation of roadways built over problematic soils.
Analysis: A meta-analysis of Early Results to predict Vaccine efficacy against Omicron

In the studies to date, the estimated fold-drop in neutralisation titre against Omicron ranges from 2- to over 20-fold depending on the study and serum tested. Collating data from the se results in a combined estimate of the fold drop in neutralisation titre against Omicron of 9.7 (95%CI 5.5-17.1). We use our previously established model to predict that six months after primary immunisation with an mRNA vaccine, efficacy for Omicron is estimated to have waned to around 40% against symptomatic and 80% against severe disease. A booster dose with an existing mRNA vaccine (even though it targets the ancestral spike) has the potential to raise efficacy to 86.2% (95% CI: 75.4-92.9) (symptomatic) and 98.2% (95% CI 90.9-99.7) (severe) against Omicron.
Novel Multiple Sclerosis Agents-Induced Cardiotoxicity

Background: Emerging novel therapeutics have been developed to hamper the progression of multiple sclerosis. However, the adverse events related to these new agents remain largely unknown. Therefore, we sought to investigate the cardiovascular complications of these drugs. Methods: Utilizing data from the U.S. Food and Drug Administration Adverse Events Reporting System, we comprehensively evaluated the cardiovascular complications of the newly FDA approved anti-multiple sclerosis agents. Disproportionality signal analysis was conducted by measuring reporting odds ratio (ROR) with 95% confidence interval of all the cardiovascular adverse events adverse events since approval till 2021. Results: After vetting the newly approved agents for multiple sclerosis, CD20 and CD25 inhibitors and sphingosine-1-phosphate receptors agonists were the latest approved medications for multiple sclerosis since 2015. Two CD20 (ocrelizumab, ofatumumab) and one CD25 inhibitors (daclizumab) were significantly associated with multiple cardiovascular adverse events. Among all the cardiotoxic events; coronary artery disease, cardiac failure and atrial fibrillation were the most predominant among CD20 or CD25 blockers. Interestingly, Sphingosine-1-phosphate receptors agonists showed much fewer reported cardiac adverse events. However, fingolimod and siponimod were associated with significant bradycardia. Conclusions: Our data revealed the new agents prescribed for multiple sclerosis have cardiotoxic effects, including not only the known adverse effects observed effects for S1P receptor modulators but also undefined cardiovascular complications associated with CD20 and CD25 inhibitors. These findings potentially instigate further studies to personalize prescribing these agents for multiple sclerosis based on patient cardiovascular profile.
Safety and Immunogenicity of a Heterologous Booster of Protein Subunit Vaccine MVC-COV1901 after Two Doses of Adenoviral Vector Vaccine AZD1222

We report the interim safety and immunogenicity results in participants administrated with a booster dose of protein subunit vaccine MVC-COV1901 at 12 or 24 weeks after two doses of AZD1222 (ChAdOx1 nCoV-19). In subjects fully vaccinated with two doses of AZD1222, waning antibody immunity was apparent within six months of the second dose of AZD1222. At one month after the MVC-COV1901 booster dose, anti-SARS-CoV-2 spike IgG antibody titers and neutralizing antibody titers were 14- and 8.6-fold increased, respectively, when compared to the titer levels on the day of the booster dose. These interim results support the use of MVC-COV1901 as a heterologous booster for individuals vaccinated with AZD1222.
MALDI-TOF mass spectrometry of saliva samples as a prognostic tool for COVID-19

ABSTRACT Background The SARS-CoV-2 infections are still imposing a great public health challenge despite the recent developments in vaccines and therapy. Searching for diagnostic and prognostic methods that are fast, low-cost and accurate are essential for disease control and patient recovery. The MALDI-TOF mass spectrometry technique is rapid, low cost and accurate when compared to other MS methods, thus its use is already reported in the literature for various applications, including microorganism identification, diagnosis and prognosis of diseases. Methods Here we developed a prognostic method for COVID-19 using the proteomic profile of saliva samples submitted to MALDI-TOF and machine learning algorithms to train models for COVID-19 severity assessment. Results We achieved an accuracy of 88.5%, specificity of 85% and sensitivity of 91.5% for classification between mild/moderate and severe conditions. When we tested the model performance in an independent dataset, we achieved an accuracy, sensitivity and specificity of 67.18, 52.17 and 75.60% respectively. Conclusion Saliva is already reported to have high inter-sample variation; however, our results demonstrates that this approach has the potential to be a prognostic method for COVID-19. Additionally, the technology used is already available in several clinics, facilitating the implementation of the method. Further investigation using a larger dataset is necessary to consolidate the technique.
SARS-CoV-2 Omicron variant escapes neutralization by vaccinated and convalescent sera and therapeutic monoclonal antibodies

The novel SARS-CoV-2 variant, Omicron (B.1.1.529) contains about 30 mutations in the spike protein and the numerous mutations raise the concern of escape from vaccine, convalescent sera and therapeutic drugs. Here we analyze the alteration of their neutralizing titer with Omicron pseudovirus. Sera of 3 months after double BNT162b2 vaccination exhibite ~27-fold lower neutralization titers against Omicron than D614G mutation. Neutralization titer is also reduced in convalescent sera from Alpha and Delta patients. However, some Delta patients have relatively preserved neutralization activity up to the level of 3-month double BNT162b2 vaccination. Omicron escapes from the cocktail of imdevimab and casirivimab, whereas sotrovimab that targets the conserved region to prevent viral escape is effective to Omicron similarly to the original SARS-CoV-2. The ACE2 decoy is another modality that neutralize the virus independently of mutational escape and Omicron is also sensitive to the engineered ACE2.
Partial discharge signal generation transmission and acquisition

The phase resolved partial discharge detection technique (or 'PD pattern recording technique') is applicable to field measurements, particularly if combined with disturbance rejection techniques and non-conventional pick-up circuits. Electromagnetic disturbances can be eliminated by filtering in the time domain (impulse noise gating) and/or in the frequency domain (down-mixing). The PD patterns not only reveal the PD physics and thus help to identify the defect type, but also serve to evaluate the efficiency of the disturbance rejection procedure.
RMM: Reinforced Memory Management for Class-Incremental Learning

Class-Incremental Learning (CIL) [40] trains classiﬁers under a strict memory budget: in each incremental phase, learning is done for new data, most of which is abandoned to free space for the next phase. The preserved data are exemplars used for replaying. However, existing methods use a static and ad hoc strategy for memory allocation, which is often sub-optimal. In this work, we propose a dynamic memory management strategy that is optimized for the incremental phases and different object classes. We call our method reinforced memory management (RMM), leveraging reinforcement learning. RMM training is not naturally compatible with CIL as the past, and future data are strictly non-accessible during the incremental phases. We solve this by training the policy function of RMM on pseudo CIL tasks, e.g., the tasks built on the data of the 0 -th phase, and then applying it to target tasks. RMM propagates two levels of actions: Level-1 determines how to split the memory between old and new classes, and Level-2 allocates memory for each speciﬁc class. In essence, it is an optimizable and general method for memory management that can be used in any replaying-based CIL method. For evaluation, we plug RMM into two top-performing baselines (LUCIR+AANets and POD+AANets [30]) and conduct experiments on three benchmarks (CIFAR-100, ImageNet-Subset, and ImageNet-Full). Our results show clear improvements, e.g., boosting POD+AANets by 3 . 6% , 4 . 4% , and 1 . 9% in the 25 -Phase settings of the above benchmarks, respectively. The code is available at https://class-il.mpi-inf.mpg. de/rmm
EFFECTS OF PEG 6000 STRESS ON STRAWBERRY (FRAGARIA × ANANASSA DUCH.) IN VITRO PROPAGATION

The aim of the study was to assess the drought tolerance of two varieties of strawberry, Tecla and Hecker, according to their reaction by morphometric characteristics to the presence of PEG 6000 in different concentrations in culture media (0 g/L control, 10 g/L, 20 g/L, 30 g/L, 40 g/L, 50 g/L). The results show that cv. Hecker exhibited the lowest value of dry weight (66.08 ± 12.17) at 50 g/L PEG concentration. The highest concentrations of PEG 6000 (40 g/L and 50 g/L) considerably decreased the DW in the plantlets in both Tecla and Hecker cvs. High concentrations of PEG 6000 decreased significantly the plantlets’ length and proliferation rate in both cultivars. A dramatic decrease (71.77%) in average height was recorded in cv. Hecker at the highest concentration of PEG 6000 (50 g/L) when compared to the control, from 8.68 ± 0.13 cm to 2.45 ± 0.19 cm (PEG 6000 50 g/L). These results suggest that PEG 6000 disrupted the water metabolism in both strawberry cultivars.
The clinical efficacy of S-1 combined with gemcitabine in senile patients with advanced pancreatic cancer and the drugs’ effects on quality of life

Objective: This study aimed to explore the clinical efficacy of S-1 combined with gemcitabine in senile patients with advanced pancreatic cancer, and the drugs’ effects on quality of life. Methods: 64 senile patients with advanced pancreatic cancer admitted to our hospital were retrospectively analyzed according to their clinicopathological characteristics and divided into a control group (n=32) and an observation group (n=32). After treatment with S-1, the control group was compared with the observation group which was treated with S-1 and gemcitabine in terms of clinical efficacy, serum natural killer T (NKT), Interferon-γ (IFN-γ), T cell subsets of immune function indexes (CD3 +, CD4 + and CD8 +), toxic reaction, quality of life (QOL), and survival time. Results: The observation group reported a higher effective rate of treatment than the control group (P<0.05), and the treatment resulted in the elevation of the NKT, IFN-γ, CD3 +, CD4 +, CD4 /CD8 +, and QOL scores, and a reduction in CD8 + in both groups, after which, the NKT, IFN-γ, CD3 +, CD4 +, and CD4 /CD8 + levels were higher, and the CD8 + level was lower in the observation group compared with the control group (P<0.05). Without any statistical differences in the toxic reaction rate calculated based on the toxic reactions including neutropenia, aleucocytosis and nausea/vomiting (P>0.05), the observation group had a longer median survival time, a median disease progression time, and a higher 1-year survival time rate than the control group (P<0.05). Conclusion: Considering, the combination of S-1 and gemcitabine compared with the application of S-1 alone, the combination of S-1 and gemcitabine can achieve a better efficacy, improve QOL, and extend the survival time of senile patients with advanced pancreatic cancer without additional adverse reactions. Those advantages are thought to be associated with the improved structures of the T-cell subsets and expressions of NKT and IFN-γ.
Enhanced performance using STBC aided coding for led‐based multiple input single output visible light communication network

Light‐emitting diode‐based (LED) visible light communication (VLC) has become a candidate in next generation ultrahigh‐speed indoor wireless local area network. Within this scenario, it is most likely for a receiver to obtain the signals from multiple LED sources, which in general brings about interference among the transmitters and may result in failure detection. In this article, space‐time block coding (STBC), as a representative multiple antenna techniques, is adopted to enhance the VLC network performance for its effective suppression on the intertransmitter interference. We experimentally demonstrate a multiple input single output VLC network consisting of two RGB‐LEDs. Using STBC‐OFDM Coding, a total throughput of 500 Mb/s is achieved and the free space transmission reach can be extended as long as 5 m. As far as we know, it is the fastest speed and longest distance in multiple antenna VLC network using STBC coding. Both theoretical simulation and experimental investigation reveal great improvements in the robustness, flexibility, and compatibility of a LED‐VLC network due to the appropriate coding scheme. © 2015 Wiley Periodicals, Inc. Microwave Opt Technol Lett 57:2943–2946, 2015
Theory-experiment comparison for the Casimir force between metallic test bodies: A spatially nonlocal dielectric response

It has been known that the Lifshitz theory of the Casimir force comes into conflict with the measurement data if the response of conduction electrons in metals to electromagnetic fluctuations is described by the well tested dissipative Drude model. The same theory is in a very good agreement with measurements of the Casimir force from graphene whose spatially nonlocal electromagnetic response is derived from the first principles of quantum electrodynamics. Here, we propose the spatially nonlocal phenomenological dielectric functions of metals which lead to nearly the same response, as the standard Drude model, to the propagating waves, but to a different response in the case of evanescent waves. Unlike some previous suggestions of this type, the response functions considered here depend on all components of the wave vector as is most natural in the formalism of specular reflection used. It is shown that these response functions satisfy the Kramers-Kronig relations. We derive respective expressions for the surface impedances and reflection coefficients. The obtained results are used to compute the effective Casimir pressure between two parallel plates, the Casimir force between a sphere and a plate, and its gradient in configurations of the most precise experiments performed with both nonmagnetic (Au) and magnetic (Ni) test bodies. It is shown that in all cases (Au-Au, Au-Ni, and Ni-Ni test bodies) the predictions of the Lifshitz theory found by using the dissipative nonlocal response functions are in as good agreement with the measurement data, as was reached previously with the dissipationless plasma model. Possible developments and applications of these results are discussed.
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation

Weakly-Supervised Semantic Segmentation (WSSS) segments objects without heavy burden of dense annotation. While as a price, generated pseudo-masks exist obvious noisy pixels, which result in sub-optimal segmentation models trained over these pseudo-masks. But rare studies notice or work on this problem, even these noisy pixels are inevitable after their improvements on pseudo-mask. So we try to improve WSSS in the aspect of noise mitigation. And we observe that many noisy pixels are of high confidences, especially when the response range is too wide or narrow, presenting an uncertain status. Thus, in this paper, we simulate noisy variations of response by scaling the prediction map in multiple times for uncertainty estimation. The uncertainty is then used to weight the segmentation loss to mitigate noisy supervision signals. We call this method URN, abbreviated from Uncertainty estimation via Response scaling for Noise mitigation. Experiments validate the benefits of URN, and our method achieves state-of-the-art results at 71.2% and 41.5% on PASCAL VOC 2012 and MS COCO 2014 respectively, without extra models like saliency detection. Code is available at https://github.com/XMed-Lab/URN.
Graph Kernel Neural Networks

The convolution operator at the core of many modern neural architectures can effectively be seen as performing a dot product between an input matrix and a filter. While this is readily applicable to data such as images, which can be represented as regular grids in the Euclidean space, extending the convolution operator to work on graphs proves more challenging, due to their irregular structure. In this paper, we propose to use graph kernels, i.e., kernel functions that compute an inner product on graphs, to extend the standard convolution operator to the graph domain. This allows us to define an entirely structural model that does not require computing the embedding of the input graph. Our architecture allows to plug-in any type and number of graph kernels and has the added benefit of providing some interpretability in terms of the structural masks that are learned during the training process, similarly to what happens for convolutional masks in traditional convolutional neural networks. We perform an extensive ablation study to investigate the impact of the model hyper-parameters and we show that our model achieves competitive performance on standard graph classification datasets.
Designing weighted and multiplex networks for deep learning user geolocation in Twitter

Predicting the geographical location of users of social media like Twitter has found several applications in health surveillance, emergency monitoring, content personalization, and social studies in general. In this work we contribute to the research in this area by designing and evaluating new methods based on the literature of weighted multigraphs combined with state-of-the-art deep learning techniques. The explored methods depart from a similar underlying structure (that of an extended mention and/or follower network) but use different information processing strategies, e.g., information diffusion through transductive and inductive algorithms -- RGCNs and GraphSAGE, respectively -- and node embeddings with Node2vec+. These graphs are then combined with attention mechanisms to incorporate the users' text view into the models. We assess the performance of each of these methods and compare them to baseline models in the publicly available Twitter-US dataset; we also make a new dataset available based on a large Twitter capture in Latin America. Finally, our work discusses the limitations and validity of the comparisons among methods in the context of different label definitions and metrics.
Scaling Up Query-Focused Summarization to Meet Open-Domain Question Answering

Query-focused summarization (QFS) requires generating a textual summary given a query using a set of relevant documents. However, in practice, such relevant documents are not readily available but should be ﬁrst retrieved from a document collection. Therefore, we show how to extend this task to make it more realistic. Thereby the task setup also resembles the settings of the open-domain question answering task, where the answer is a summary of the top-retrieved documents. To address this extended task, we combine passage retrieval with text generation to produce the summary of the retrieved passages given the input query. We demonstrate the ﬁrst evaluation results on the proposed task and show that a few samples are suﬃcient to ﬁne-tune a large generative model with retrieved passages.
Optimal Memory Scheme for Accelerated Consensus Over Multi-Agent Networks

Consensus overmulti-agent networks can be accelerated by utilizing agent’s memory to the control protocol. In this paper, a general protocol with memory information from the node and its neighbors is designed. We aim to provide an optimal memory scheme to accelerate consensus. The contributions of this paper include: (i) For the one-tap memory scheme, we prove that the memory information of neighbors is unnecessary for the optimal convergence. (ii) It is proved that in the worst-case scenario, one-tap node memory is sufficient to achieve the optimal convergence rate, that is, adding more taps of past information from neighbors can not improve the rate further. (iii) It is found that the convergence rate with two-tap memory can be further improved on star networks. Numerical examples are presented to illustrate the validity and correctness of the obtained results.