Implementation of Static and Semi-Static Versions of a 24+8x8 Quad-rail NULL Convention Multiply and Accumulate Unit

This paper focuses on implementing a 2s complement 8x8 dual-rail bit-wise pipelined multiplier using the asynchronous null convention logic (NCL) paradigm. The design utilizes a Wallace tree for partial product summation, and is implemented and simulated in VHDL, the transistor level, and the physical level, using a 1.8V 0.18mum TSMC CMOS process. The multiplier is realized using both static and semi-static versions of the NCL gates; and these two implementations are compared in terms of area, power, and speed

Implementation of Static and Semi-Static Versions of a Bit-wise Pipelined Dual-rail NCL 2S Complement Multiplier

This paper focuses on implementing a 2s complement 8x8 dual-rail bit-wise pipelined multiplier using the asynchronous NULL Convention Logic (NCL) paradigm. The design utilizes a Wallace tree for partial product summation, and is implemented and simulated in VHDL, the transistor level, and the physical level, using a 1.8V 0.18,um TSMC CMOS process.The multiplier is realized using both static and semi-static Dualversions of the NCL gates; and these two implementations are compared in terms of area, power, and speed

The Need for Inducing Tolerance in Vascularized Composite Allotransplantation

Successful hand and face transplantation in the last decade has firmly established the field of vascularized composite allotransplantation (VCA). The experience in VCA has thus far been very similar to solid organ transplantation in terms of the morbidity associated with long-term immunosuppression. The unique immunological features of VCA such as split tolerance and resistance to chronic rejection are being investigated. Simultaneously there has been laboratory work studying tolerogenic protocols in animal VCA models. In order to optimize VCA outcomes, translational studies are needed to develop less toxic immunosuppression and possibly achieve donor-specific tolerance. This article reviews the immunology, animal models, mixed chimerism & tolerance induction in VCA and the direction of future research to enable better understanding and wider application of VCA

Assimilation of Remote Sensing Data into Crop Growth Model for Yield Estimation: A Case Study from India

Crop yield estimation is important to inform logistics management such as the prescription of nutrient inputs, financing, storage and transport, marketing as well as to inform for crop insurance appraisals due to loss incurred by abiotic and biotic stresses. In this study, we used a suite of methods to assess yields at the village level (\5 km2) using remote sensing technology and crop modeling in Indian states of Telangana, Andhra Pradesh and Odisha. Remote sensing products were generated using Sentinel-2 and Landsat 8 time series data and calibrated with data collected from farmers’ fields. We derived maps showing spatial variation in crop extent, crop growth stages and leaf area index (LAI), which are crucial in yield assessment. Crop classification was performed on Sentinel-2 time series data using spectral matching techniques (SMTs) and crop management information collected from field surveys along with ground data. The locations of crop cutting experiments (CCEs) was identified based on crop extent maps. LAI was derived based on the SAVI (soil-adjusted vegetation index) equation were using Landsat 8-time series data. We used the technique of re-parametrization of crop simulation models based on the several iterations using remote sensing leaf area index (LAI). The data assimilation approach helps in fine-tuning the initial parameters of the crop growth model and improving simulation with the help of remotely sensed observations. Results clearly show a good correlation between observed and simulated crop yields (R2 is greater than 0.7) for all the crops studied. Our study showed that by assimilation of remotely sensed data in to crop models, crop yields at harvest could be successfully predicted

Global Mental Health and Nutrition: Moving Toward a Convergent Research Agenda.

Both malnutrition and poor mental health are leading sources of global mortality, disease, and disability. The fields of global food security and nutrition (FSN) and mental health have historically been seen as separate fields of research. Each have undergone substantial transformation, especially from clinical, primary care orientations to wider, sociopolitical approaches to achieve Sustainable Development Goals. In recent years, the trajectories of research on mental health and FSN are further evolving into an intersection of evidence. FSN impacts mental health through various pathways such as food insecurity and nutrients important for neurotransmission. Mental health drives FSN outcomes, for example through loss of motivation and caregiving capacities. They are also linked through a complex and interrelated set of determinants. However, the heterogeneity of the evidence base limits inferences about these important dynamics. Furthermore, interdisciplinary projects and programmes are gaining ground in methodology and impact, but further guidance in integration is much needed. An evidence-driven conceptual framework should inform hypothesis testing and programme implementation. The intersection of mental health and FSN can be an opportunity to invest holistically in advancing thinking in both fields

Development of eAgromet Prototype to Improve the Performance of Integrated Agromet Advisory Service

In several countries, the systems for forecasting weather are being operated to deal with weather and its related factors affecting agricultural production. India meteorological department (IMD) is providing several types of weather forecasts. One of the forecast service is medium range forecast (MRF). As a part of MRF, the expected values of rain fall, temperature, cloud cover, humidity, wind speed and wind direction for next five days are forecasted twice a week by considering district as a unit. Agriculture is markedly affected by weather condition during crop season. IMD in collaboration with Indian Council of Agriculture Research (ICAR) and State Agriculture Universities (SAUs) has set-up about 130 Agro-meteorological Field Units (AMFUs) and each AMFU covers about five districts. Based on MRF, IMD is rendering Integrated Agromet Advisory Service to the farming community of the country in the form of agromet advisory bulletin. The agromet advisory bulletins contain possible risk mitigation measures for the major crops and livestock. Based on the weather forecast, a group of interdisciplinary scientists and agromet scientists at AMFU prepare district-level agromet advisory bulletins. These bulletins are sent to the farmers and other stakeholders of the corresponding district. To ease the process of preparing agromet bulletins, an effort has started to build IT-based agrometeorological advisory system called, eAgromet. In this paper, we explain the concepts of eAgromet and its operation

The achievement of food and nutrition security in South Asia is deeply gendered

Women form an integral part of the agricultural sector, and in much of South Asia women make up a majority of the agricultural workforce, often compelled to work in order to meet their families’ basic needs. While their contributions are recognised as central to the food and nutrition security of households and communities, their work is not recognized or supported adequately by public policy and social institutions. Women continue to face inequality across key development indicators including health, education, and nutrition; discriminatory laws; and high levels of precarity in terms of income, employment conditions, safety and wellbeing. Social structures that promote gender inequality and inhibit the agency of women contribute to the South Asian enigma – the persistence of undernutrition despite economic growth – and must be addressed to achieve food and nutrition security

Systemic Delivery of an Adjuvant CXCR4-CXCL12 Signaling Inhibitor Encapsulated in Synthetic Protein Nanoparticles for Glioma Immunotherapy

Glioblastoma (GBM) is an aggressive primary brain cancer, with a 5 year survival of ∼5%. Challenges that hamper GBM therapeutic efficacy include (i) tumor heterogeneity, (ii) treatment resistance, (iii) immunosuppressive tumor microenvironment (TME), and (iv) the blood-brain barrier (BBB). The C-X-C motif chemokine ligand-12/C-X-C motif chemokine receptor-4 (CXCL12/CXCR4) signaling pathway is activated in GBM and is associated with tumor progression. Although the CXCR4 antagonist (AMD3100) has been proposed as an attractive anti-GBM therapeutic target, it has poor pharmacokinetic properties, and unfavorable bioavailability has hampered its clinical implementation. Thus, we developed synthetic protein nanoparticles (SPNPs) coated with the transcytotic peptide iRGD (AMD3100-SPNPs) to target the CXCL2/CXCR4 pathway in GBM via systemic delivery. We showed that AMD3100-SPNPs block CXCL12/CXCR4 signaling in three mouse and human GBM cell cultures in vitro and in a GBM mouse model in vivo. This results in (i) inhibition of GBM proliferation, (ii) reduced infiltration of CXCR4+ monocytic myeloid-derived suppressor cells (M-MDSCs) into the TME, (iii) restoration of BBB integrity, and (iv) induction of immunogenic cell death (ICD), sensitizing the tumor to radiotherapy and leading to anti-GBM immunity. Additionally, we showed that combining AMD3100-SPNPs with radiation led to long-term survival, with ∼60% of GBM tumor-bearing mice remaining tumor free after rechallenging with a second GBM in the contralateral hemisphere. This was due to a sustained anti-GBM immunological memory response that prevented tumor recurrence without additional treatment. In view of the potent ICD induction and reprogrammed tumor microenvironment, this SPNP-mediated strategy has a significant clinical translation applicability.Fil: Alghamri, Mahmoud S.. University Of Michigan Medical School; Estados UnidosFil: Banerjee, Kaushik. University Of Michigan Medical School; Estados UnidosFil: Mujeeb, Anzar A.. University Of Michigan Medical School; Estados UnidosFil: Mauser, Ava. University of Michigan; Estados UnidosFil: Taher, Ayman. University Of Michigan Medical School; Estados UnidosFil: Thalla, Rohit. University Of Michigan Medical School; Estados UnidosFil: McClellan, Brandon L.. University Of Michigan Medical School; Estados UnidosFil: Varela, Maria L.. University Of Michigan Medical School; Estados UnidosFil: Stamatovic, Svetlana M.. University Of Michigan Medical School; Estados UnidosFil: Martinez Revollar, Gabriela. University Of Michigan Medical School; Estados UnidosFil: Andjelkovic, Anuska V.. University Of Michigan Medical School; Estados UnidosFil: Gregory, Jason V.. University of Michigan; Estados UnidosFil: Kadiyala, Padma. University Of Michigan Medical School; Estados UnidosFil: Calinescu, Alexandra. University Of Michigan Medical School; Estados UnidosFil: Jiménez, Jennifer A.. University of Michigan; Estados UnidosFil: Apfelbaum, April A.. University of Michigan; Estados UnidosFil: Lawlor, Elizabeth R.. University of Washington; Estados UnidosFil: Carney, Stephen. University of Michigan; Estados UnidosFil: Comba, Andrea. University Of Michigan Medical School; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Faisal, Syed Mohd. University Of Michigan Medical School; Estados UnidosFil: Barissi, Marcus. University Of Michigan Medical School; Estados UnidosFil: Edwards, Marta B.. University Of Michigan Medical School; Estados UnidosFil: Appelman, Henry. University Of Michigan Medical School; Estados UnidosFil: Sun, Yilun. Case Western Reserve University; Estados UnidosFil: Gan, Jingyao. University of Michigan; Estados UnidosFil: Ackermann, Rose. University of Michigan; Estados UnidosFil: Schwendeman, Anna. University of Michigan; Estados UnidosFil: Candolfi, Marianela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; ArgentinaFil: Olin, Michael R.. University of Minnesota; Estados UnidosFil: Lahann, Joerg. University of Michigan; Estados UnidosFil: Lowenstein, Pedro R.. University of Michigan; Estados UnidosFil: Castro, Maria G.. University of Michigan; Estados Unido