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Yields of groundnut, the traditional grain legume grown in central and southern Cameroon and in much of the humid zone of Central Africa, are generally low. Other food legumes may provide alternatives to groundnut. On-farm experiments examined the relative yields of up to 15 pigeonpea, 10 groundnut, 7 soybean, and 4 cowpea varieties over three growing seasons in four to six rural communities. Soil analytical values and rainfall data from all seasons were used as covariates in the analysis of variance. In the first two trials, variety-within-species interactions were significant (P < 0.0001 and 0.04). Groundnut var. JL-24 yielded 60% more than local groundnuts in the first season of 2000, while soybean var. TGx1838-5E, local cowpea var. 'Mefak' and pigeonpea var. ICEAP 00436 outyielded several other varieties of their respective species. Comparing these selected varieties over three seasons, significant species × community and species × season effects (P < 0.0001) were observed. Covariate analysis showed that soybean yields increased with increasing soil Mg saturation and P levels. Groundnut yielded more in the first season of 2000 compared to the second seasons of 1999 and 2000 (average yields of 927 kg ha⁻¹ vs. 422 and 522 kg ha⁻¹, respectively). Improved yields were related to soil exchangeable Ca levels greater than 5 cmol(+) kg⁻¹ in both second seasons, but not during the first season. Cowpea yields were superior in both second seasons. Pigeonpea yields were unrelated to soil factors, showing its wide adaptability to soil conditions. Pigeonpea, which matured in February during the dry season, was severely affected by the early cessation of rains in 2000. In 1999 yields averaged 820 kg ha⁻¹ across communities. The results show that good food legume alternatives to groundnut exist, particularly for second season production. Species can be targeted to communities based on soil properties and season of production.

In radio interferometry, observed visibilities are intrinsically sampled at some interval in time and frequency. Modern interferometers are capable of producing data at very high time and frequency resolution; practical limits on storage and computation costs require that some form of data compression be imposed. The traditional form of compression is a simple averaging of the visibilities over coarser time and frequency bins. This has an undesired side effect: the resulting averaged visibilities \"decorrelate\", and do so differently depending on the baseline length and averaging interval. This translates into a non-trivial signature in the image domain known as \"smearing\", which manifests itself as an attenuation in amplitude towards off-centre sources. With the increasing fields of view and/or longer baselines employed in modern and future instruments, the trade-off between data rate and smearing becomes increasingly unfavourable. In this work we investigate alternative approaches to low-loss data compression. We show that averaging of the visibility data can be treated as a form of convolution by a boxcar-like window function, and that by employing alternative baseline-dependent window functions a more optimal interferometer smearing response may be induced. In particular, we show improved amplitude response over a chosen field of interest, and better attenuation of sources outside the field of interest. The main cost of this technique is a reduction in nominal sensitivity; we investigate the smearing vs. sensitivity trade-off, and show that in certain regimes a favourable compromise can be achieved. We show the application of this technique to simulated data from the Karl G. Jansky Very Large Array (VLA) and the European Very-long-baseline interferometry Network (EVN).

The desire for wide-field of view, large fractional bandwidth, high sensitivity, high spectral and temporal resolution has driven radio interferometry to the point of big data revolution where the data is represented in at least three dimensions with an axis for spectral windows, baselines, sources, etc; where each axis has its own set of sub-dimensions. The cost associated with storing and handling these data is very large, and therefore several techniques to compress interferometric data and/or speed up processing have been investigated. Unfortunately, averaging-based methods for visibility data compression are detrimental to the data fidelity, since the point spread function (PSF) is position-dependent, i.e. distorted and attenuated as a function of distance from the phase centre. The position dependence of the PSF becomes more severe, requiring more PSF computations for wide-field imaging. Deconvolution algorithms must take the distortion into account in the major and minor cycles to properly subtract the PSF and recover the fidelity of the image. This approach is expensive in computation since at each deconvolution iteration a distorted PSF must be computed. We present two algorithms that approximate these position-dependent PSFs with fewer computations. The first algorithm approximates the position-dependent PSFs in the \\(uv\\)-plane and the second algorithm approximates the position-dependent PSFs in the image-plane. The proposed algorithms are validated using simulated data from the MeerKAT telescope.

This work proposes to reduce visibility data volume using a baseline-dependent lossy compression technique that preserves smearing at the edges of the field-of-view. We exploit the relation of the rank of a matrix and the fact that a low-rank approximation can describe the raw visibility data as a sum of basic components where each basic component corresponds to a specific Fourier component of the sky distribution. As such, the entire visibility data is represented as a collection of data matrices from baselines, instead of a single tensor. The proposed methods are formulated as follows: provided a large dataset of the entire visibility data; the first algorithm, named \\(simple~SVD\\) projects the data into a regular sampling space of rank\\(-r\\) data matrices. In this space, the data for all the baselines has the same rank, which makes the compression factor equal across all baselines. The second algorithm, named \\(BDSVD\\) projects the data into an irregular sampling space of rank\\(-r_{pq}\\) data matrices. The subscript \\(pq\\) indicates that the rank of the data matrix varies across baselines \\(pq\\), which makes the compression factor baseline-dependent. MeerKAT and the European Very Long Baseline Interferometry Network are used as reference telescopes to evaluate and compare the performance of the proposed methods against traditional methods, such as traditional averaging and baseline-dependent averaging (BDA). For the same spatial resolution threshold, both \\(simple~SVD\\) and \\(BDSVD\\) show effective compression by two-orders of magnitude higher than traditional averaging and BDA. At the same space-saving rate, there is no decrease in spatial resolution and there is a reduction in the noise variance in the data which improves the S/N to over \\(1.5\\) dB at the edges of the field-of-view.

Traditional radio interferometric correlators produce regular-gridded samples of the true \\(uv\\)-distribution by averaging the signal over constant, discrete time-frequency intervals. This regular sampling and averaging then translate to be irregular-gridded samples in the \\(uv\\)-space, and results in a baseline-length-dependent loss of amplitude and phase coherence, which is dependent on the distance from the image phase centre. The effect is often referred to as \"decorrelation\" in the \\(uv\\)-space, which is equivalent in the source domain to \"smearing\". This work discusses and implements a regular-gridded sampling scheme in the \\(uv\\)-space (baseline-dependent sampling) and windowing that allow for data compression, field-of-interest shaping and source suppression. The baseline-dependent sampling requires irregular-gridded sampling in the time-frequency space i.e. the time-frequency interval becomes baseline-dependent. Analytic models and simulations are used to show that decorrelation remains constant across all the baselines when applying baseline-dependent sampling and windowing. Simulations using MeerKAT telescope and the European Very Long Baseline Interferometry Network show that both data compression, field-of-interest shaping and outer field-of-interest suppression are achieved.

We present MeerKAT 1.28 GHz total-intensity, polarization, and spectral-index images covering the giant (projected length \\(l \\approx 1.57\\)~Mpc) X-shaped radio source PKS~2014\\(-\\)55 with an unprecedented combination of brightness sensitivity and angular resolution. They show the clear \"double boomerang\" morphology of hydrodynamical backflows from the straight main jets deflected by the large and oblique hot-gas halo of the host galaxy PGC~064440. The magnetic field orientation in PKS~2014\\(-\\)55 follows the flow lines from the jets through the secondary wings. The radio source is embedded in faint (\\(T_\\mathrm{b} \\approx 0.5 \\mathrm{\\,K}\\)) cocoons having the uniform brightness temperature and sharp outer edges characteristic of subsonic expansion into the ambient intra-group medium. The position angle of the much smaller (\\(l \\sim 25\\)~kpc) restarted central source is within \\(5^\\circ\\) of the main jets, ruling out models that invoke jet re-orientation or two independent jets. Compression and turbulence in the backflows probably produce the irregular and low polarization bright region behind the apex of each boomerang as well as several features in the flow with bright heads and dark tails.

The new generation of radio interferometers is characterized by high sensitivity, wide fields of view and large fractional bandwidth. To synthesize the deepest images enabled by the high dynamic range of these instruments requires us to take into account the direction-dependent Jones matrices, while estimating the spectral properties of the sky in the imaging and deconvolution algorithms. In this paper we discuss and implement a wide-band wide-field spectral deconvolution framework (DDFacet) based on image plane faceting, that takes into account generic direction-dependent effects. Specifically, we present a wide-field co-planar faceting scheme, and discuss the various effects that need to be taken into account to solve for the deconvolution problem (image plane normalization, position-dependent PSF, etc). We discuss two wide-band spectral deconvolution algorithms based on hybrid matching pursuit and sub-space optimisation respectively. A few interesting technical features incorporated in our imager are discussed, including baseline dependent averaging, which has the effect of improving computing efficiency. The version of DDFacet presented here can account for any externally defined Jones matrices and/or beam patterns.

The LOFAR Two-metre Sky Survey (LoTSS) is an ongoing sensitive, high-resolution 120-168MHz survey of the entire northern sky for which observations are now 20% complete. We present our first full-quality public data release. For this data release 424 square degrees, or 2% of the eventual coverage, in the region of the HETDEX Spring Field (right ascension 10h45m00s to 15h30m00s and declination 45\\(^\\circ\\)00\\('\\)00\\(''\\) to 57\\(^\\circ\\)00\\('\\)00\\(''\\)) were mapped using a fully automated direction-dependent calibration and imaging pipeline that we developed. A total of 325,694 sources are detected with a signal of at least five times the noise, and the source density is a factor of \\(\\sim 10\\) higher than the most sensitive existing very wide-area radio-continuum surveys. The median sensitivity is S\\(_{\\rm 144 MHz} = 71\\,\\mu\\)Jy beam\\(^{-1}\\) and the point-source completeness is 90% at an integrated flux density of 0.45mJy. The resolution of the images is 6\\(''\\) and the positional accuracy is within 0.2\\(''\\). This data release consists of a catalogue containing location, flux, and shape estimates together with 58 mosaic images that cover the catalogued area. In this paper we provide an overview of the data release with a focus on the processing of the LOFAR data and the characteristics of the resulting images. In two accompanying papers we provide the radio source associations and deblending and, where possible, the optical identifications of the radio sources together with the photometric redshifts and properties of the host galaxies. These data release papers are published together with a further \\(\\sim\\)20 articles that highlight the scientific potential of LoTSS.

Introduction: Poliovirus (PV) and non-polio enteroviruses (NPEV) belong to the Picornaviridae family. They are found worldwide and are responsible for a wide range of diseases such as acute flaccid paralysis (AFP). This study aimed to evaluate the detection rate of PV and NPEV in stool samples from children under fifteen years of age presenting with AFP in Cameroon and their distribution over time. Methodology: Stool samples were collected as part of poliovirus surveillance throughout Cameroon from 2015 to 2020. Virus isolation was performed using RD and L20B cells maintained in culture. Molecular methods such as intratypic differentiation were used to identify PVs serotypes and analysis of the VP1 genome was performed. Results: A total of 12,354 stool samples were analyzed. The EV detection rate by virus isolation was 11.42% (1411/12354). This rate varied from year to year with a mean distribution of 11.41 with a 95% confidence interval [11.37; 11.44]. Of the viruses detected, suspected poliovirus accounted for 31.3% (442/1411) and NPEV 68.67% (969/1411). No wild poliovirus (WPV) was isolated. Sabin types 1 and 3 were continuously isolated. Surprisingly, from February 2020, vaccine-derived PV type 2 (VDPV2) was detected in 19% of cases, indicating its resurgence. Conclusions: This study strongly supports the successful elimination of WPV in Cameroon and the resurgence of VDPV2. However, as long as VDPV outbreaks continue to be detected in Africa, it remains essential to monitor how they spread.

Venous thromboembolism (VTE) is a serious complication in hospitalized patients. It is associated with considerable morbidity and mortality. Therefore, its prevention is of great importance. There is paucity of data on the incidence of VTE in hospitalized patients in Cameroon. The aim of this study was to determine the incidence of symptomatic VTE, its risk factors and the proportion of patients at risk that receive thromboprophylaxis in patients hospitalized in the medical and surgical units in two hospitals in the South West Region of Cameroon. A prospective study was performed in the medical and surgical units from January to March 2018. All consecutive eligible patients admitted for at least 3 days were included. Patient profile and risk factors were recorded. Patients were followed and evaluated for signs and symptoms of VTE until discharge from hospital. Suspected VTE was confirmed using compression ultrasonography and computed tomography. A total of 314 patients were included of which 58.7% were females. The mean age was 46±17.9 years. Patients aged <40 years represented 42% of the study population. Three cases of symptomatic VTE were recorded. The incidence of symptomatic VTE was 1% (95% CI: 0.3-2.8%). The prevalence of VTE risk was 93.6% with 32.5% being at high risk. The risk was 94.6% in medical patients and 92.8% in surgical patients. Among the patients at risk, only 32.5% received thromboprophylaxis. Thromboprophylaxis was significantly higher in surgical patients compared to medical patients (45.2% versus 18.7%; p<0.0001). The incidence of VTE in hospitalized medical and surgical patients appeared low but likely underestimated considering the high prevalence of patients at risk of VTE coupled with the underutilization of thromboprophylaxis. Clinicians should assess risk of VTE in conjunction with the clinical situation to determine the most appropriate type of prophylaxis as well as the duration of prophylaxis for VTE.