bluuebunny/crossref_metadata_2025 huggingface dataset:
Design paths of federal intergovernmental cooperation
Authors: Montfort Simon , Fischer Manuel , Hollway James , Jager Nicolas W.
Published: March 2023

Actors rarely approach institutional design choices with a blank slate but are influenced by design choices made at earlier stages. How does institutional design evolve over time and are there specific paths to deepening cooperation? We investigate the institutional design paths of subnational cooperation that are chosen to address increasingly complex and interconnected policy problems. We theorize that besides the substantive problem, earlier choices matter to explain what institutional design mechanism is chosen; that is, the design of existing institutions between two subnational governance units, called substates, influences the design of subsequent institutions. Using a semi‐parametric Cox proportional hazards model, we show that the design paths of subnational cooperation in the Swiss water governance sector correlate with earlier design choices. Our results indicate that not all cooperation is self‐reinforcing and path‐dependent, but they show which specific design choices are more likely to follow each other in repeated formal federal intergovernmental cooperation.
Full‐length EFOP3 and EFOP4 proteins are essential for pollen intine development in Arabidopsis thaliana
Authors: Chen Xinlong , Zhang Yingying , Yin Wuzhong , Wei Gang , Xu Hailing , Ma Lu , Tian Weijiang , Yang Guang , Li Yunfeng , Wu Renhong , Zhang Ting , Wang Nan , He Guanghua
Published: March 2023

Pollen development is critical to plant reproduction, but the underlying regulatory molecular mechanisms have not been fully elucidated. The Arabidopsis (Arabidopsis thaliana) EFR3 OF PLANT 3 (EFOP3) and EFR3 OF PLANT 4 (EFOP4) genes encode members of the Armadillo (ARM) repeat superfamily that play key roles in pollen development. Herein, we demonstrate that EFOP3 and EFOP4 are co‐expressed in pollen at anther stages 10–12, but loss‐of‐function of both EFOP3 and EFOP4 leads to male gametophyte sterility, irregular intine, and shriveled pollen grains at anther stage 12. We further established that full‐length EFOP3 and EFOP4 specifically localize to the plasma membrane, and the integrity of these proteins is essential for pollen development. We observed uneven intine, less organized cellulose and reduced pectin content in mutant pollen compared with the wild‐type. These, together with the misexpression of several genes related to cell wall metabolism in efop3−/− efop4+/− mutants, suggest that EFOP3 and EFOP4 may indirectly regulate the expression of these genes to affect intine formation, thus controlling Arabidopsis pollen fertility in a functionally redundant manner. Moreover, transcriptome analysis showed that the absence of EFOP3 and EFOP4 function affects multiple pollen development pathways. These results enhance our understanding of EFOPs proteins and their role in pollen development.
Imaging features of benign synovial inflammation affecting the long digital extensor tendon sheath in a dog
Authors: Scharf Alexandra , Husbands Brian D. , Tremolada Giovanni , Hostnik Eric T.
Published: March 2023

A dog was evaluated for right pelvic limb lameness. Computed tomography and magnetic resonance imaging revealed an irregular, contrast‐enhancing mass extending along the proximolateral right tibia, involving the long digital extensor tendon (LDET) ± cranial tibialis muscle. Pulmonary nodules, nonspecific hepatomegaly, and splenic nodules were also present. The primary differential diagnosis was soft tissue neoplasia. Surgical biopsy with histopathology revealed benign, chronic inflammation, and fibrosis. Idiopathic synovial inflammation should be included as a differential diagnosis for dogs with this combination of clinical and imaging characteristics.
Seed germination of common and endangered arable weed species is differently affected by the herbicide metazachlor and its transformation products
Authors: García Andrés , Eichberg Carsten , Wendell Anne‐Kathrin , Pfeifer Simon , Ludewig Kristin , Donath Tobias W. , Ulrich Uta
Published: March 2023

High inputs of herbicides have caused the decline of arable weed species, which formerly evolved under less intensively managed crop lands. Arable weed species have adapted their germination strategies to respond to unfavourable periods. However, species considered endangered have more specific germination requirements. The herbicide metazachlor (MZ), in its formulated form Butisan®, is used for the control of annual and dicotyledonous weeds and degrades in the soil into two major transformation products (TPs): metazachlor oxalic acid (MZ‐OA) and metazachlor ethane sulphonic acid (MZ‐ESA). These TPs are often found in higher concentrations compared to MZ. We tested the effect of MZ and its TP on seed germination of four common and three endangered arable weed species present in Central Europe by setting up multi‐factorial climate chamber experimental designs. We chose concentrations imitating field conditions and according to previously reported fraction occurrences in the topsoil. We assessed germination by measuring germination percentage, mean germination time and synchrony of germination. All germination variables were affected with increasing concentrations of MZ. Both, common and endangered species showed a decrease in germination percentage under the highest concentration of MZ. MZ and MZ‐OA increased the synchronicity of endangered species, while both TPs decreased mean germination time of endangered species. Common species showed less significant differences when compared to the control and also had a less synchronised and longer germination time compared to endangered species. The observed response of common species to both MZ and TPs may be convenient in the face of short term unfavourable environmental conditions, such as those found in intensively managed arable lands. Our results highlight that the species‐specific response of arable weeds to herbicides will further increase the dominance of common over rare arable weed species.
Actualidad del discernimiento de los signos de los tiempos de Marie-Dominique Chenu
Authors: Espinosa Dumar
Published: March 2023

La lectura de los “signos de los tiempos”, concepto determinante en la teología de la segunda mitad del siglo XX, orientó el camino renovador del Concilio Vaticano II bajo el impulso del magisterio de Juan XXIII, pero es formalmente mencionada solo una vez en el documento final debido a las objeciones bíblicas y cristológicas de teólogos católicos y protestantes. En el posconcilio, dicho concepto permitió la renovación de la teología latinoamericana y mundial porque creó las bases de una teología inductiva. A pesar de su aportación teológica, el concepto se ha estereotipado, malinterpretado y banalizado.El objetivo de la investigación es identificar el sentido y el alcance del término en Marie-Dominique Chenu, influyente teólogo del Concilio Vaticano II que abanderó el pretendido aggiornamento desde la lectura de los signos de los tiempos. Antes del Concilio, este método le permitió desarrollar teologías de las realidades terrestres como la teología del trabajo. A partir del análisis de diversos escritos del autor, se identifica la naturaleza de los signos de los tiempos como criterio para su discernimiento en teologías atentas tanto al dato revelado como al devenir de la historia, se mencionan las objeciones a su uso en el ambiente conciliar y se analiza la pertinencia del uso del término signos de los tiempos por parte de algunos autores al referirse a todo tipo de situaciones cambiantes de la actualidad.Ahora que la expresión “lectura de los signos de los tiempos” es leitmotiv de teólogos y pastores cercanos al pueblo, impulsados por la novedad del papa Francisco y en la línea de la Gaudium et spes, conviene revisar la historia del concepto, precisar su alcance y evitar su uso indiscriminado. Chenu continúa siendo maestro probado para tal operación; su atención a la historia enseña la función concienciadora que toda teología encarnada debiera promover.
Thermodynamic and Kinetic Modeling Directs Pathway Optimization for Isopropanol Production in a Gas-Fermenting Bacterium
Authors: Lo Jonathan , Wu Chao , Humphreys Jonathan R. , Yang Bin , Jiang Zhenxiong , Wang Xin , Maness PinChing , Tsesmetzis Nicolas , Xiong Wei
Published: March 2023

Highly efficient bioproduction from gaseous substrates (e.g., hydrogen and carbon oxides) will require systematic optimization of the host microbes. To date, the rational redesign of gas-fermenting bacteria is still in its infancy, due in part to the lack of quantitative and precise metabolic knowledge that can direct strain engineering.
Effects of Lumacaftor-Ivacaftor on Airway Microbiota-Mycobiota and Inflammation in Patients with Cystic Fibrosis Appear To Be Linked to Pseudomonas aeruginosa Chronic Colonization
Authors: Enaud Raphael , Lussac-Sorton Florian , Charpentier Elena , Velo-Suárez Lourdes , Guiraud Jennifer , Bui Stéphanie , Fayon Michael , Schaeverbeke Thierry , Nikolski Macha ,   the LumIvaBiota Study Group, Burgel Pierre-Régis , Héry-Arnaud Geneviève , Delhaes Laurence
Published: March 2023

The management of cystic fibrosis has been transformed recently by the advent of CFTR modulators, including lumacaftor-ivacaftor. However, the effects of such therapies on the airway ecosystem, particularly on the microbiota-mycobiota and local inflammation, which are involved in the evolution of pulmonary damage, are unclear.
Flufenamic Acid, a Promising Agent for the Sensitization of Colistin-Resistant Gram-Negative Bacteria to Colistin
Authors: Zhang Yi , Han Yijia , Wang Lingbo , Kong Jingchun , Pan Wei , Zhang Xiaodong , Chen Lijiang , Yao Zhuocheng , Zhou Tieli , Cao Jianming
Published: March 2023

Colistin is a last-line antibiotic used for the treatment of MDR Gram-negative bacterial infections. However, increasing resistance to it has been observed during clinical treatment.
Diagnostic dilemma: our experience with necrotising sialometaplasia
Authors: Mohanraj Lekhaa , Singh Urvashi , Lakshmanan Somu
Published: March 2023

A man in his 40s presented to our outpatient department with a painful ulcer in the oral cavity for 1 week. After intraoral examination, a single hard palate ulcer, which was non-tender on palpation, was noted. Baseline blood investigations such as haemogram and serological evaluation were within normal limits. Under local anaesthesia, an excisional biopsy was performed. The histopathological examination revealed a reactive necrotising inflammatory process involving minor salivary glands with no cytological atypia. Weekly follow-up was performed and at the end of 4 weeks, complete healing of the lesion had occurred without any further intervention.
Simultaneous excision of pelvic lipoma and robot-assisted radical prostatectomy
Authors: Dranova Sabina , Carbin Danny Darlington , Perry Matthew , Abou Chedid Wissam
Published: March 2023

Lipoma is a benign mesenchymal tumour that can develop in any part of the body containing adipose tissue. Very few cases of pelvic lipomas have been reported in the literature. Due to their location and slow growth, pelvic lipomas are often asymptomatic for a prolonged time. Thus, on diagnosis, they are usually found to be of considerable size. Due to their size, pelvic lipomas can present causing bladder outlet obstruction, lymphoedema, abdominal and pelvic pain, and constipation, as well as present with deep vein thrombosis (DVT) like symptoms. Patients with cancer have a much higher risk of developing DVT. Here, we describe a case of an incidental finding of pelvic lipoma mimicking DVT in a patient with organ-confined prostate cancer. He eventually underwent simultaneous robot-assisted radical prostatectomy and lipoma excision.
Comparison of carotid endarterectomy and repeated carotid angioplasty and stenting for in-stent restenosis (CERCAS trial): a randomised study
Authors: Hrbáč Tomáš , Fiedler Jiří , Procházka Václav , Jonszta Tomáš , Roubec Martin , Pakizer David , Václavík Daniel , Netuka David , Heryán Tomáš , Školoudík David
Published: March 2023

Background and aim

In-stent restenosis (ISR) belongs to an infrequent but potentially serious complication after carotid angioplasty and stenting in patients with severe carotid stenosis. Some of these patients might be contraindicated to repeat percutaneous transluminal angioplasty with or without stenting (rePTA/S). The purpose of the study is to compare the safety and effectiveness of carotid endarterectomy with stent removal (CEASR) and rePTA/S in patients with carotid ISR.

Methods

Consecutive patients with carotid ISR (≥80%) were randomly allocated to the CEASR or rePTA/S group. The incidence of restenosis after intervention, stroke, transient ischaemic attack myocardial infarction and death 30 days and 1 year after intervention and restenosis 1 year after intervention between patients in CEASR and rePTA/S groups were statistically evaluated.

Results

A total of 31 patients were included in the study; 14 patients (9 males; mean age 66.3±6.6 years) were allocated to CEASR and 17 patients (10 males; mean age 68.8±5.6 years) to the rePTA/S group. The implanted stent in carotid restenosis was successfully removed in all patients in the CEASR group. No clinical vascular event was recorded periproceduraly, 30 days and 1 year after intervention in both groups. Only one patient in the CEASR group had asymptomatic occlusion of the intervened carotid artery within 30 days and one patient died in the rePTA/S group within 1 year after intervention. Restenosis after intervention was significantly greater in the rePTA/S group (mean 20.9%) than in the CEASR group (mean 0%, p=0.04), but all stenoses were <50%. Incidence of 1-year restenosis that was ≥70% did not differ between the rePTA/S and CEASR groups (4 vs 1 patient; p=0.233).

Conclusion

CEASR seems to be effective and save procedures for patients with carotid ISR and might be considered as a treatment option.

Trial registration number

NCT05390983.
Marine and freshwater centrohelid heliozoans (Haptista: Centroplasthelida) in Canada, including taxonomic revisions and descriptions of 22 new species and subspecies
Authors: Nicholls Kenneth H.
Published: March 2023

Canadian coastal waters of the Pacific Ocean and the Atlantic Ocean as well as inland freshwater habitats in Ontario were sampled for centrohelid heliozoans (free-living heterotrophic single-celled organisms) over a period of nearly five decades. More than 60 species and subspecies were revealed, including 1 Triangulopteris, 1 Raphidocystis, 2 Pseudoraphidocystis, 3 Raineriophrys, 4 Pseudoraphidiophrys, 12 Choanocystis, 15 Pterocystis, and 25 Acanthocystis taxa. Of these, 22 were officially named and described as new to science based primarily on the morphology of the siliceous scales that cover the cell. New species were compared with images and/or descriptions of close “relatives” to validate their new species assignments. New data on five species of Acanthocystis and one species of Choanocystis required revisions of their descriptive taxonomy that in some cases resulted in the splitting off of separate species or subspecies. Very little can be concluded about global distribution of centrohelid heliozoans, owing to the paucity of records. For many of the taxa reported here, previous records consist of just one or two findings from other parts of the world. One example is Choanocystis antarctica Tikhonenkov and Mylnikov, 2011 that was previously known only from Antarctic seawater, but is reported here from an Ontario softwater lake.
Indirect dark-matter detection with MadDM v3.2 – Lines and Loops
Authors: Arina Chiara , Heisig Jan , Maltoni Fabio , Massaro Daniele , Mattelaer Olivier
Published: March 2023

Automated tools for the computation of particle physics’ processes have become the backbone of phenomenological studies beyond the standard model. Here, we present MadDM v3.2. This release enables the fully automated computation of loop-induced dark-matter annihilation processes, relevant for indirect detection observables. Special emphasis lies on the annihilation into $$\gamma X$$

γ
X

, where $$X=\gamma , Z, h$$

X
=
γ
,
Z
,
h

or any new particle even under the dark symmetry. These processes lead to the sharp spectral feature of monochromatic gamma lines – a smoking-gun signature of dark matter in our Galaxy. MadDM provides the predictions for the respective fluxes near-Earth and derives constraints from the gamma-ray line searches by Fermi-LAT and HESS. As an application, we discuss the implications for the viable parameter space of a top-philic t-channel mediator model and the inert doublet model.
Thermal instability analysis in magneto-hybrid nanofluid layer between rough surfaces with variable gravity and space-dependent heat source
Authors: Rana Puneet , Gupta Vishal , Kumar Lokendra
Published: March 2023

The onset of thermal instability with hybrid nanoparticles Al2O3–Cu in nanofluid is investigated with the combined effects of variable gravity, variable heat source and Lorentz force in a porous medium. Its applications are in many areas like chemical engineering, geophysics, astrophysics, etc. Based on literature, many gravity variations are assumed in the present analysis, along with a space-dependent heat source/sink parameter that varies along the width of the channel. The finite difference-based Lobatto IIIa method has been applied to solve the system under no nanoparticle flux and rough boundary conditions, and approximate analytical results are generated for some special cases. The roughness parameters ([Formula: see text]), Darcy number (Da), gravity variation function ([Formula: see text]), and Chandrasekhar number (Q) delay the onset of convection and thus stabilize the system. It is also observed that an increase in the Lewis number Le, power index in variable heat source, and the nanoparticle Rayleigh concentration number Rn decreases the critical Rayleigh number [Formula: see text] which destabilizes the system, and increases the critical wave number, which enlarges the convection cell size. In the case of exponential variation ([Formula: see text]) in the gravity variation parameter, the system becomes stabilized due to a delay in the onset of convection. In addition, we have considered multilayer perceptron-artificial neural network (MLP-ANN) computation to predict the critical Rayleigh number as per function of important controlling parameters. The data set of 625 observations is chosen keeping 70% for testing, 15% for training and 15% for validation using efficient Levenberg−Marquardt back propagation algorithm with optimal accuracy measures i.e., root mean square deviation (RMSE), root mean relative error (RMRE) and [Formula: see text] (coefficient of determination). Finally, the regression plots are drawn that correlate, target and output data.
More Than Just Algorithms
Authors: Spector Alfred , Norvig Peter , Wiggins Chris , Wing Jeannette , Fried Ben , Tingley Michael
Published: March 2023

Dramatic advances in the ability to gather, store, and process data have led to the rapid growth of data science and its mushrooming impact on nearly all aspects of the economy and society. Data science has also had a huge effect on academic disciplines with new research agendas, new degrees, and organizational entities. The authors of a new textbook, Data Science in Context: Foundations, Challenges, Opportunities, share their ideas about the impact of the field on nearly all aspects of the economy and society.
Astaxanthin Alleviates Inflammatory Response in Neonatal Necrotizing Enterocolitis Rats by Regulating NOD2/TLR4 Pathway
Authors: Zhang Xuandong , Luo Yujia , Gu Rui , Jiang Zhou
Published: March 2023

Background. Necrotizing enterocolitis (NEC) is often associated with exaggerated activation of inflammatory response. Astaxanthin has been shown in studies to have a positive and advantageous effect on anti-inflammatory response. Hence, it is of great significance to study the protective effect of astaxanthin in NEC disease and its molecular mechanism. Objective. The present study was to investigate whether astaxanthin attenuates NEC rats and to explore its potential mechanism. Material and Methods. Hematoxylin-eosin staining was used to observe the pathological change of the intestinal tissue in NEC rats. Subsequently, we determined the anti-oxidative stress, anti-apoptosis, and anti-inflammation in astaxanthin with enzyme-linked immunosorbent assay kits, TUNEL staining, western blot, and immunohistochemistry assay. Furthermore, we added nucleotide-binding oligomerization domain 2 (NOD2) inhibitor to certify the molecular pathway of the astaxanthin in NEC rats. Results. Astaxanthin improved the pathological changes of the intestinal tissues. It restrained inflammation, oxidative stress, and protected cells from apoptosis in the intestinal tissue and serum of the NEC rats. Moreover, astaxanthin enhanced NOD2, whereas it suppressed toll-like receptor 4 (TLR4), nuclear factor-κB (NF-κB) pathway-related proteins. Apart from that, the NOD2 inhibitor offset the protective effect of the astaxanthin towards the NEC rats. Conclusion. The present study indicated that astaxanthin alleviated oxidative stress, inflammatory response, and apoptosis in NEC rats by enhancing NOD2 and inhibiting TLR4 pathway.
Large B-cell Lymphomas of Immune-Privileged Sites Relapse via Parallel Clonal Evolution from a Common Progenitor B Cell
Authors: Los-de Vries G. Tjitske , Stathi Phylicia , Rutkens Ryanne , Hijmering Nathalie J. , Luijks Jeroen A.C.W. , Groenen Patricia J.T.A. , de Jong Daphne , Ylstra Bauke , Roemer Margaretha G.M.
Published: March 2023

Large B-cell lymphoma of immune-privileged sites (LBCL-IP) arise in immune sanctuaries including the testis and central nervous system (CNS). After initially reaching complete response, relapses occur in almost 50% of patients, typically at other immune-privileged sites. Resolution of the clonal relationships and evolutionary patterns of LBCL-IP is required to understand the unique clinical behavior. We collected a unique set of 33 primary–relapse LBCL-IP sample pairs and performed next-generation sequencing for copy number, mutation, translocation, and immunoglobulin clonality analysis. All LBCL-IP sample pairs were clonally related, and both tumors developed from a common progenitor cell (CPC) with MYD88 and TBL1XR1 mutations and/or BCL6 translocations in 30/33 cases, indicating that these are early genetic events. This was succeeded by intermediate genetic events including shared, as well as unique alterations in targets of aberrant somatic hypermutation (aSHM), CD79B mutations, and 9p21.3/CDKN2A loss. Genetic alterations in genes involved in immune escape (HLA, CD274/PDCD1LG2) were predominantly unique in primary and relapse samples and thus considered late genetic events. Together, this study indicates that primary and relapsed LBCL-IP follow an early parallel evolutionary pattern where the CPC contains genetic alterations that support prolonged survival/proliferation and retention in a memory B-cell state, followed by germinal center reentry, aSHM and immune escape.

Significance:

Genomic analyses reveal that primary and relapse LBCL-IP originate from a common progenitor cell with a small set of genetic alterations, followed by extensive parallel diversification, elucidating the clonal evolution of LBCL-IP.
Terminally Exhausted CD8+ T Cells Resistant to PD-1 Blockade Promote Generation and Maintenance of Aggressive Cancer Stem Cells
Authors: Chakravarti Mohona , Dhar Sukanya , Bera Saurav , Sinha Abhipsa , Roy Kamalika , Sarkar Anirban , Dasgupta Shayani , Bhuniya Avishek , Saha Akata , Das Juhina , Banerjee Saptak , Vernekar Manisha , Pal Chiranjib , Alam Neyaz , Datta Dipak , Baral Rathindranath , Bose Anamika
Published: March 2023

Heterogeneity within the tumor-infiltrating lymphocytes (TIL) population limits immunotherapeutic efficacy against cancer. Between two subpopulations of exhausted CD8+ TILs (progenitor-exhausted; TPEX, terminally exhausted; TTEX), TTEX cells remain unresponsive to anti–programmed cell death protein 1(PD-1) therapy. Deciphering whether and how PD-1–resistant TTEX cells engage in tumor promotion could improve the response to immunotherapy. Here, we report that TTEX cells actively participate in tumor progression by modulating cancer stem cells (CSC). TTEX cells strongly correlated with elevated CSC frequency in poorly immune-infiltrated (CD8+ TIL low) advanced human breast and ovarian carcinomas. TTEX directly upregulated CSC frequency in vitro, which was not affected by anti–PD-1 treatment. The TTEX-influenced CSCs were highly clonogenic and exhibited a multidrug-resistant phenotype, overexpressing drug efflux pumps like ABCC1 and ABCB1. These CSCs were highly invasive, displaying increased invadopodia development and elevated cofilin, CXCR4, and matrix metalloproteinase 7 (MMP7) expression. The invasive properties along with epithelial–mesenchymal plasticity of TTEX-educated CSCs increased metastasis in vivo. TTEX increased cell surface levels and activation of VEGFR2 in CSCs, and silencing or inhibition of VEGFR2 reversed the CSC-stimulatory effects of TTEX. LAMP3 and NRP1 on the surface of TTEX stimulated VEGFR2 in CSCs to promote aggressiveness. Cumulatively, these findings suggest that screening patients with carcinoma for both CD8+ TILs and TTEX frequency prior to anti–PD-1 therapy could improve patient outcomes. In addition, targeting the LAMP3/NRP1–VEGFR2 axis could be a therapeutic strategy in advanced patients with carcinoma with limited CD8+ T-cell infiltration and high TTEX frequency.

Significance:

Cross-talk with TTEX CD8+ T cells mediated by the VEGFR2 axis induces aggressive properties in cancer stem cells to promote tumor progression.
A Phase II Study of Epacadostat and Pembrolizumab in Patients with Advanced Sarcoma
Authors: Kelly Ciara M. , Qin Li-Xuan , Whiting Karissa A. , Richards Allison L. , Avutu Viswatej , Chan Jason E. , Chi Ping , Dickson Mark A. , Gounder Mrinal M. , Keohan Mary Louise , Movva Sujana , Nacev Benjamin A. , Rosenbaum Evan , Adamson Travis , Singer Samuel , Bartlett Edmund K. , Crago Aimee M. , Yoon Sam S. , Hwang Sinchun , Erinjeri Joseph P. , Antonescu Cristina R. , Tap William D. , D'Angelo Sandra P.
Published: March 2023

Purpose:

Epacadostat, an indole 2,3 dioxygenase 1 (IDO1) inhibitor, proposed to shift the tumor microenvironment toward an immune-stimulated state, showed early promise in melanoma but has not been studied in sarcoma. This study combined epacadostat with pembrolizumab, which has modest activity in select sarcoma subtypes.

Patients and Methods:

This phase II study enrolled patients with advanced sarcoma into five cohorts including (i) undifferentiated pleomorphic sarcoma (UPS)/myxofibrosarcoma, (ii) liposarcoma (LPS), (iii) leiomyosarcoma (LMS), (iv) vascular sarcoma, including angiosarcoma and epithelioid hemangioendothelioma (EHE), and (v) other subtypes. Patients received epacadostat 100 mg twice daily plus pembrolizumab at 200 mg/dose every 3 weeks. The primary endpoint was best objective response rate (ORR), defined as complete response (CR) and partial response (PR), at 24 weeks by RECIST v.1.1.

Results:

Thirty patients were enrolled [60% male; median age 54 years (range, 24–78)]. The best ORR at 24 weeks was 3.3% [PR, n = 1 (leiomyosarcoma); two-sided 95% CI, 0.1%–17.2%]. The median PFS was 7.6 weeks (two-sided 95% CI, 6.9–26.7). Treatment was well tolerated. Grade 3 treatment-related adverse events occurred in 23% (n = 7) of patients. In paired pre- and post-treatment tumor samples, no association was found between treatment and PD-L1 or IDO1 tumor expression or IDO-pathway–related gene expression by RNA sequencing. No significant changes in serum tryptophan or kynurenine levels were observed after baseline.

Conclusions:

Combination epacadostat and pembrolizumab was well tolerated and showed limited antitumor activity in sarcoma. Correlative analyses suggested that inadequate IDO1 inhibition was achieved.
Parental Psychological Distress of Missed Diagnosis of Down Syndrome at Antenatal Screening: A Rare, but Still Real Occurrence – A Case Report and Review of Literature
Authors: Giuseppe Macri Pasquale , Tosto Valentina , Tsibizova Valentina , Meyyazhagan Arun , Di Renzo Gian Carlo
Published: March 2023

Introduction: Prenatal screening programs are important components for pregnant women care and are often linked with grief and shock based on gestational age or the diagnosis. Lower/no sensitivity is also associated with these screening programs leading to providing false-negative outputs. Case Presentation: Present work shows a case of missed antenatal diagnosis of Down syndrome and its persistant medical and psychological impact on the family members. We have also discussed the relevant economic and medical-legal issues related to the context and aimed to maintain an adequate awareness among healthcare to discuss properly these investigations (difference between screening and diagnostic testing), their possible outcome (chances of false results) and enabled the pregnant women/couple to take informed decision on early pregnancy. Conclusion: These programs are considered as routine clinical practice in many countries from last few years and are necessary to assess the pros and cons of these programs. One of the prime cons involves the likeliness of obtaining a false-negative result due to lack of 100% sensitivity and specificity.
Earthquake prediction technique: a comparative study
Authors: Badr Jarah Nada , Hassin Alasadi Abbas H. , Mahdi Hashim Kadhim
Published: March 2023

Earthquakes are one of the most dangerous natural disasters facing humans because of their occurrence without warning and their impact on their lives and property. In addition, predicting seismic movement is one of the main research topics in seismic disaster prevention. In geological studies, scientists can predict and know the locations of earthquakes in the long term. Therefore, about 80% of the major global earthquakes lie along the Pacific Ring belt, known as the Ring of Fire. Machine learning methods have also been used for short-term earthquake prediction, and studies have applied the random forest method to determine the factors that precede earthquakes. The machine learning method was based on various decision trees, each of which predicted the time to the nearest oscillation. The third group of scientists used the hybrid prediction method, which combines machine learning and geological studies. This research deals with a review of most of the geological studies and machine learning techniques applied to earthquake data sets, which showed a total lack of prediction of potential earthquakes through one approach, so studies designed by geologists were combined with machine learning.
Classification of cardiac disorders based on electrocardiogram data using a decision tree classification approach with the C45 algorithm
Authors: Sumiati Sumiati , Ronald Repi Viktor Vekky , Hendriyati Penny , Anharudin Anharudin , Yusta Afrasim , Triayudi Agung
Published: March 2023

The limitations of medical personnel, especially heart disease, cause difficulties in diagnosing heart disorders, so diagnosing heart disorders is not easy, it takes the ability and experience of a cardiologist who has the expertise and experience to be able to accurately diagnose heart disorders. Several studies in the field of computing have been carried out in diagnosing cardiac abnormalities in patients. This study was conducted to accurately test the results of the classification of heart disorders using electrocardiogram medical record data with a C.45 decision tree approach. The results showed that the classification of heart defects obtained a mean squared error (MSE) value of 0.24, a root mean squared error (RMSE) value of 0.49, and an accuracy value of 75.33% with the C4.5 algorithm.
Comparative study of surface roughness prediction using neural-network and quadratic-rotatable-central-composite-design
Authors: Princess Okokpujie Imhade , Kwanda Tartibu Lagouge
Published: March 2023

The act of sustainable manufacturing lies in the response's prediction analysis, such as surface roughness during machining operations with nano-lubricant. This research focuses on developing a mathematical model to predict the experimental results of surface roughness of AA8112 alloys obtained during the end-milling process with an eco-friendly nano-lubricant. The study employed vegetable oil as the base cutting fluid (copra oil) and Titanium-dioxide (TiO2) nanoparticles as an additive. The end-milling machining was carried out with five machining parameters. The prediction analysis was done with a backpropagation feed-forward neural network (BPNN) and quadratic rotatable central composite design (QRCCD). The results show that the BPNN predicted the experimental results with 99.85%, and the QRCCD predicted 91.1%. The error percentage from both prediction analyses is 0.2% from the BPNN and 0.9% from the QRCCD. Therefore, the application of BPNN has proven viable in predicting surface roughness in machining operations. It will also improve the manufacturing industry's productivity and eliminate the high rate of waste materials during machining.
Neural network models selection scheme for health mobile app development
Authors: Sudarya Triana Yaya , Azam Osman Mohd , Pratomo Adji , Fermi Pasha Muhammad , Stiawan Deris , Budiarto Rahmat
Published: March 2023

Mobile healthcare application (mHealth app) assists the frontline health worker in providing necessary health services to the patient. Unfortunately, existing mHealth apps continue to have accuracy issues and limited number of disease detection systems. Thus, an intelligent disease diagnostics system may help medical staff as well as people in poor communities in rural areas. This study proposes a scheme for simultaneously selecting the best neural network models for intelligent disease detection systems on mobile devices. To find the best models for a given dataset, the proposed scheme employs neural network models capable of evolving altered neural network architectures. Eight neural network models are developed simultaneously and then implemented on the Android Studio platform. Mobile health applications use pre-trained neural network models to provide users with disease prediction results. The performance of the mobile application is measured against the existing available datasets. The trained neural network engines perform admirably, detecting 7 out of 8 diseases with high accuracy ranging from 86.00% to 100% and a low detection accuracy of 63%. The detection times vary from 0.01 to 0.057 seconds. The developed mHealth app may be used by health workers and patients to improve resource-poor community health services and patients' healthcare quality.
Artificial intelligence-based lead propensity prediction
Authors: Jadli Aissam , Hain Mustapha , Hasbaoui Anouar
Published: March 2023

Lead propensity prediction is a data-driven method used to define the value of prospects, by assigning points to them based on their engagement with the business's digital channels, based on multiple key attributes correlating to their attraction to the proposed services or items. The resulting score is closely related to the financial worth of each lead and may be revealing its position in the buying cycle. The marketing teams can then focus on generated leads and prioritize the most prominent ones to improve the conversion rates, using the assigned score on the lead scoring step. The authors investigated using a combination of a data-driven approach and Artificial intelligence (AI) techniques for the lead-scoring process. The experimentation shows that the random forest (RF) is the most suitable model for this task with an accuracy score of 93.04% followed by the decision tree (DT) model of 91.47%. In contrast, when considering the training time, DT and logistic regression (LR) needed a shorter time to learn from the dataset while maintaining decent performances. In contrast, these models represent promising alternatives to the RF model especially in the case of a huge volume of transactions and prospects or in a big data context.
Fabric defect classification using transfer learning and deep learning
Authors: Beljadid Aafaf , Tannouche Adil , Balouki Abdessamad
Published: March 2023

The internal inspection of fabrics is one of the most important phases of production in order to achieve high quality standard in the textile industry. Therefore, developing efficient automatic internal control mechanism has been an extremely major area of research. In this paper, the famous architecture Googlenet was fine-tuned into two configurations for texture defect classification that was trained on a textile texture database (TILDA). The experimental result, for both configurations, achieved a significant overall accuracy score of 97% for motif and a non-motif-based images and 89% for mixed images. In the results obtained, it was observed that the second model, which updates the last six layers, was more successful than the first one; which updates the last two layers.
Text detection and recognition through deep learning-based fusion neural network
Authors: Kumar Dasari Sunil , Mehta Shilpa
Published: March 2023

Text recognition task involves recognizing the text from the natural image; it possesses various application, which aids information extraction through data mining from street view like images. Scene text recognition involves two stages i.e., text detection and text recognition, in the past several mechanisms has been proposed for accurate identification, these mechanisms are either traditional approach or deep learning-based. All the existing deep-learning methodology fails as this comprises character data and image data, further this research develops an optimal architecture fusion neural network (FNN) for text identification and recognition. FNN comprises several layers of convolutional neural network as well as recurrent neural network. Within FNN architecture convolutional layer is utilized for the feature extraction and recurrent layer is utilized for attaining the feature classification prediction. Further, an optimal training architecture is established for the enhancement of classification accuracy. Here Devanagari MLT-19 dataset is utilized for the evaluation of FNN. Three different parameters are considered during evaluation i.e., script word identification, character recognition rate (CRR) and word recognition rate (WRR). Further comparison with existing models is performed to establish the proposed model efficiency and it shows FNN methodology observes 98.67% of script identification accuracy, 84.65% of WRR and 92.93% of CRR.
Store product classification using convolutional neural network
Authors: Wiryana I Made , Harmanto Suryadi , Fauzi Alfharizky , Bil Qisthi Imam , Nadya Utami Zalita
Published: March 2023

Stores sells consumer goods, mainly food products and other household products at retail. The products sold in stores vary greatly, in order to be time efficient in the fast-paced era and the current technological era requires artificial intelligence technology. In the artificial intelligence branch, there is a specific or detailed learning process known as deep learning. One of the branches of deep learning is the convolutional neural network(CNN). This research intends to employ a CNN architecture to facilitate and streamline the time and cost of the store’s product sorting process. The test is conducted with 1,050 product images divided into 35 labels and divided into three data, namely 80% data training 10% data validation and 10% data test. The image used is preprocessed with a size of 256×256 pixels. The data was trained with six convolution layers and an epoch of 50 with an execution time of 33 minutes so as to achieve an accuracy of 91.37%.
Online panel data quality: a sentiment analysis based on a deep learning approach
Authors: Ibtissam Youb , Abdallah Azmani , Mohamed Hamlich
Published: March 2023

The rise of online access panels has profoundly changed the market research landscape. Often presented by their owners as very powerful tools, they nevertheless raise important scientific questions, particularly regarding the representativeness of the samples they produce and, consequently, the validity of the information they provide. In this paper, we present an innovative approach, based on deep learning and sentiment analysis techniques, to assess in real time the representativeness of an online panel sample. The idea is to measure the extent to which the opinions of an online panel converge with opinions on social networks. To validate the proposed method, we conducted a case study on the emerging discussion on Coronavirus disease (COVID-19) vaccination. The results not only proved the representativeness of online panel sample, but also demonstrated the feasibility and effectiveness of our approach.
Lexicon-based sentiment analysis for Kannada-English code-switch text
Authors: Chundi Ramesh , R. Hulipalled Vishwanath , Bharthish Simha Jay
Published: March 2023

Sentiment analysis is the process of computationally recognizing and classifying the attitudes conveyed in each text towards a particular topic, product, etc. which is either positive or negative. Sentiment analysis is one of the interesting applications of natural language processing (NLP) and which is used to analyze the social media. Text in social media is casual and it can be written either in code-switch or monolingual text. Several researchers have implemented sentiment analysis on monolingual text, though sentiments can be expressed in code-switch text. Sentiment analysis can be applied through deep learning (DL), machine learning (ML), or a Lexicon-based approach. Machine learning (ML) and deep learning (DL) methods are time-consuming, computationally expensive, and need training data for analysis. Lexicon-based method does not require training data and requires less time to find the sentiments in comparison with ML and DL. In this paper, we propose the Lexicon-based approach (NBLex) to analyze the sentiments expressed in Kannada-English code-switch text. This is the first effort that targets to perform sentiment analysis in Kannada-English code-switch text using the Lexicon-based approach. The proposed approach performed with better accuracy of 83.2% and 83% of F1-score.
Experiment study of an automatic guided vehicle robot
Authors: Ha Vo Thu , Thuong Than Thi , Ha Vo Thanh
Published: March 2023

This paper presents the design and control of an autonomous robot-automation guided vehicle (AGV) with a load of 50 kg and a DC motor in theory and experiment. This robot has the function of transporting tools and equipment in the factory. This robot is designed and built control system including hardware and software. In which microcontroller type STM32F407VG is selected to control the primary system. The AGV robot is controlled to move according to the required trajectory. A PID controller controls the DC motor. The AGV robot moves precisely according to the routes set in the factory. At the same time, the AGV has good obstacle avoidance. The results of the proposed solution are proven through simulation and experiment.
Integrated motor drive for vehicle electrification: a step toward a more sustainable and efficient transportation system
Authors: Sutikno Tole , Padmanaban Sanjeevikumar
Published: March 2023

The transportation sector is a big source of greenhouse gas (GHG) emissions. It is responsible for about 20% of all carbon dioxide (CO2) emissions in the world, and most of those emissions come from road transportation. The development of greater efficiency vehicles with much reduced fuel consumption and GHG emissions is critical for vehicle electrification and a sustainable transportation system. Electric vehicles (EVs) offer higher energy efficiency than internal combustion engines and emit no CO2 during operation. For future vehicle electrification, cost-effective electric powertrain options are required. The current state of development of two major components of the EV powertrain: motors and drives, is introduced in this editorial.
Simulation and optimization of hydrogen consumption in a fuel cell/battery hybrid vehicle
Authors: Touileb Rachid , Abbou Ahmed
Published: March 2023

This paper presents a physics-based mathematical model for a fuel cell hybrid vehicle system. The performance design of fuel cell hybrid electric vehicles (FCHEVs) is an area of interest in transportation applications.  FCHEV is a combination of a proton exchange membrane fuel cell (PEMFC), with a battery and associated DC/DC and DC/AC converters. Suitable batteries are used in FCHEV acts as a backup system with efficient energy management. The battery is designed for fast power transfer during transient response and constant performance without hydrogen. The power management schemes discussed are consistent with the most common modern power management techniques used in fuel cell vehicle applications, including: the state engine control strategies, rule-based fuzzy logic strategies, classical proportional integral control strategies, frequency fuzzy logic decoupling/control minimization strategies. Therefore, we tested and evaluated the reliability of the model using the MATLAB/Simulink environment, with mixed results.
Demand side management in a microgrid to reduce the peak consumption cost
Authors: Mishra Debani Prasad , Panda Bhabani Shankar , Mallik Nitish Saswat , Kar Swarnodeep , Giri Nimay Chandra , Salkuti Surender Reddy
Published: March 2023

Microgrid stands for the participation of renewable sources such as solar and wind in the existing energy system in order to increase the availability and reliability of energy for consumers and to make the atmosphere carbon-free by reducing dependence on the main grid. However, the non-linearities inherent in the nature of the microgrid and network components make the programming process complex. Therefore, the efficient but linear model for microgrid resource planning algorithms is gaining interest today due to its simplicity and computational speed. On the contrary, for demand-side management, reducing the peak demand price by using different methods such as load trimming and valley filling by varying the use of flexible loads on the consumer side, which is an easier option for microgrid operators, rush hour price for consumer satisfaction. This paper gives an idea of the above purpose by designing an average model of a solar microgrid with the implementation of the maximum power point tracking (MPPT) algorithm method for stability of the system frequency and a battery storage system with a controller. You then plan the microgrid using the demand side management (DSM) method in order to reduce the cause of the price spikes.
A novel step-up/step-down DC-DC converter based on flyback and SEPIC topologies with improved voltage gain
Authors: Mahafzah Khaled A. , Rababah Hana A.
Published: March 2023

A novel step-up/step-down DC-DC converter with improved voltage gain characteristics is suggested in this paper. The proposed converter combines flyback and single-ended primary inductance converter (SEPIC) converters. The voltage control loop design is simplified due to the use of only a single controlled switch. It has been discussed how switch duty cycle affects voltage gain. This relationship demonstrates that the proposed converter voltage gain is improved compared with flyback and SEPIC converters. However, compared to flyback and SEPIC converters, it has a higher voltage gain at any given duty cycle. The proposed converter operation is thoroughly discussed, the associated equations are derived, and its parameters are expertly designed. Moreover, it is designed to supply a DC load of 2.5 kW, 500 V, and 5 A. The proposed converter efficiency exceeds 84% under rated load conditions. Additionally, it is also presented as an adapter for electric vehicles. The waveforms associated with the simulation of the converter across the universal line voltage of 110-260 Vrms are shown. At both line voltages, the total harmonics distortions (THDs) of the line current are 17.12% and 27.26%, respectively. MATLAB/Simulink is utilized for validation in order to validate the topology for different applications.